研究者詳細 - 田向　権

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AN IMPROVEMENT OF POSE RECOGNITION SYSTEM CONSIDERING INFLUENCE OF LIGHTING ENVIRONMENT 査読有り

Yamao K., Tamukoh H., Kubota R.

ICIC Express Letters, Part B: Applications ( ICIC International 学会 ) 15 ( 9 ) 883 - 888 2024年09月

記述言語：英語掲載種別：研究論文（学術雑誌）

In this paper, we propose an improving method for raising the accuracy of camera-based pose recognition system in back-lighting environments. The proposed method uses a multi-scale retinex with color restoration (MSRCR) for contrast enhancement, and a bilateral filter (BF) for noise removal to the input image as sequential preprocessing. After preprocessing, the proposed method classifies the human pose in the input image by using PoseNet, which is one of the machine learning-based vision models. PoseNet extracts 17 key-points, e.g., joints of arms and legs of person, from an input image, and outputs the coordinates of their key-points. The validity and the effectiveness of the proposed system have been verified by some experiments.

DOI： 10.24507/icicelb.15.09.883

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記憶読み出しの頻度を考慮した脳型記憶システムのFPGA実装

水谷彰伸，田中悠一朗，田向権，立野勝巳，森江隆

cross-disciplinary workshop on computing systems, infrastructures, and programming (xSIG), 2024年08月

CMOS Digital-Analog Mixed Signal VLSI Implementation of a Hippocampus-Inspired Model 査読有り国際誌

Yuka Shishido, Osamu Nomura, Katsumi Tateno, Hakaru Tamukoh and Takashi Morie

International Joint Conference on Neural Networks (IJCNN 2024) 2024年07月

A Hippocampus-Inspired Environment-Specific Knowledge Acquisition System Utilizing Common Knowledge with Contextual Information 査読有り国際誌

Akinobu Mizutani, Yuichiro Tanaka, Hakaru Tamukoh, Osamu Nomura, Katsumi Tateno and Takashi Morie

International Joint Conference on Neural Networks (IJCNN 2024) 2024年07月

Robust Binary Encoding for Ternary Neural Networks Toward Deployment on Emerging Memory 査読有り国際誌

Ninnart Fuengfusin, Hakaru Tamukoh, Osamu Nomura and Takashi Morie

International Joint Conference on Neural Networks (IJCNN2024) 2024年06月

Recognizing Nursing Activities in Endotracheal Suction: Utilizing Multiple Readouts Reservoir Computing and Large Language Models 査読有り国際誌

Arie Rachmad Syulistyo, Yuichiro Tanaka, Hakaru Tamukoh

6th International Conference on Activity and Behavior Computing (ABC 2024) 2024年05月

Enhancing Memory Capacity of Reservoir Computing with Delayed Input and Efficient Hardware Implementation with Shift Registers 査読有り国際誌

Soshi Hirayae, Kanta Yoshioka, Atsuki Yokota, Ichiro Kawashima, Yuichro Tanaka, Yuichi Katori, Osamu Nomura, Takashi Morie and Hakaru Tamukoh

IEEE International Symposium on Cirucuits and Systems (ISCAS2024) 2024年05月

A General Purpose Service Robot System Capable of Handling Commands Containing Abstract Nouns 査読有り国際誌

Kosei Yamao,Daiju Kanaoka,Kosei Isomoto, and Hakaru Tamukoh

2024 IEEE International Conference on Robotics and Automation (ICRA 2024) as Late Breaking Results Poster, Kanagawa 2024年05月

解析的な物体認識データセット開発手法の提案

鈴木章央(九工大), 山口直紀(九工大), 下田瑞穂(九工大), 田向権(九工大),

電子情報通信学会スマートインフォメディアシステム研究会（SIS） vol.123 ( SIS-440 ) 39 - 44 2024年03月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）

本研究では，機械学習に用いたときに，人が作成するデータセットと同等の精度を得られるデータセットを自動生成する手法の実現を目指す．特に画像を解析しながらデータセットを構築する手法に注目して取り組む．具体的には，生成に用いる画像を解析し，生成方法を設計，データセット生成後に生成したデータセットを解析する，というものである．本稿ではこの一連の処理に対してトイデータや軽量なアルゴリズムを採用し，まずはツールチェーンの構築に取り組む．評価実験では，データセットの解析値や学習した結果を使って定量的・定性的にツールの機能や提案手法の学習への効果を確認した．

Improvement of Read-Out Circuit for Non-volatile Memory Embedded In-Memory Computing Architectures 査読有り国際誌

Tomoro Marcus Jones, Yuka Shishido, Osamu Nomura, Hakaru Tamukoh, Takashi Morie

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Grounding DINO Fine-tuning using Images Generated by 3D Dataset Generator 査読有り国際誌

Naoki Yamaguchi, Kosei Isomoto, Tomoya Shiba, Hakaru Tamukoh

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Visual Prompting to Interpret Object Locations for Service Robots 査読有り国際誌

Kosei Yamao, Daiju Kanaoka, Kosei Isomoto, Hakaru Tamukoh

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

A Preliminary Study of Anomaly Detection of Object Grasping Points Using CNT-PDMS Nanocomposite Sensor, 査読有り国際誌

Shoshi Tokuno, Kouki Kimizuka, Yuichiro Tanaka, Hirofumi Tanaka, Hakaru Tamukoh

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

American Sign Language Recognition by Reservoir Computing with a Combination of Multi-reservoir and Self-organizing Multi-readout 査読有り国際誌

Arie Rachmad Syulistyo, Yuichiro Tanaka, Hakaru Tamukoh

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Hammering test system using Reservoir Computing 査読有り国際誌

Naoki Miyamura, Hakaru Tamukoh

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Performance Evaluation of Techniques for Enhancing Memory Capacity of Reservoir Computing 査読有り国際誌

Atsuki Yokota, Ichiro Kawashima, Hakaru Tamukoh, Osamu Nomura, Takashi Morie

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

A Brain-Inspired Artificial Intelligence Model to Acquire Positive and Negative Episodes with Hippocampal Replay Function 査読有り国際誌

Akinobu Mizutani, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Osamu Nomura, Takashi Morie

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Detecting operating status of factory automation machines using reservoir computing 査読有り国際誌

Kazuhiro Hiratsuka, Hakaru Tamukoh,

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

High Sensitivity Motion Capture Under Low-light Conditions Using EVS 査読有り国際誌

Ryuta Toyoda, Sansei Hori, Hakaru Tamukoh

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Evaluation of Lane Detection Based on Missing Lane-Specific Dataset 査読有り国際誌

Kazuma Sakakibara, Yuga Yano, Yukiya Fukuda, Hakaru Tamukoh

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Application of a Hippocampus-inspired Model toward Episodic Memory-based Navigation for Autonomous Mobile Robots 査読有り国際誌

Kosei Isomoto, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Osamu Nomura, Takashi Morie

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Integration of Cropped Image and Skeleton Information for Hand Waving Recognition by Reservoir Computing 査読有り国際誌

Hiromasa Yamaguchi, Akinobu Mizutani, Arie Rachmad Syulistyo, Yuichiro Tanaka, Hakaru Tamukoh

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Analysis of Modular Reservoir Computing Using Chaotic Boltzmann Machines 査読有り国際誌

Kazuo Nakahara, Osamu Nomura, Hakaru Tamukoh, Yuichi Katori, Takashi Morie

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

An Efficient Spatial Representation Method for Memory-based Hippocampus-inspired Model, 査読有り国際誌

Yuka Shishido, Osamu Nomura, Katsumi Tateno, Hakaru Tamukoh, Takashi Morie

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Evaluation of feature extractor trained by grayscale images for industrial anomaly detection 査読有り国際誌

Sansei Hori, Kazumichi Tanaka, Keiichi Nakanishi, Terumasa Tokunaga

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

Reinforcement Learning of Two Continuous Tasks by Reservoir Computing with Multiple Readouts 査読有り国際誌

Yuichiro Tanaka, Hakaru Tamukoh

The 5th International Symposium on Neuromorphic AI Hardware 2024年03月

リザバー計算の記憶容量増大手法の性能評価

横田篤紀，川島一郎，田向権，野村修，森江隆

第71回応用物理学会春季学術講演会 A-7 25 - 31 2024年03月

YOLO real-time object detection on EV3-Robot using FPGA hardware Accelerator 査読有り

Pramanta Dinda, Fuengfusin Ninnart, Syulistyo Arie Rachmad, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 29 416 - 420 2024年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

The growing demand for robots necessitates faster and more precise processing. However, running large Artificial Intelligence (AI) models from cloud data centers to mobile robots via inference models uses considerable computation resources, which leads to power limitations, particularly for mobile robots. The use of reconfigurable semiconductor devices at the hardware level is a promising solution to this problem. We introduce the educational kit EV3-Robot with a co-design methodology utilizing Field-programmable Gate Arrays (FPGA) Kria KV260 as a hardware accelerator specifically for object detection. We apply the You Only Look Once (YOLO) model for object detection, which provides real-time results for practical applications. Additionally, we analyze the processing times of the local PC and EV3-Robot.

DOI： 10.5954/icarob.2024.os15-1

RoboCup@Home 2023: Stickler for the Rules Task Solutions 査読有り

Shiba Tomoya, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 29 435 - 437 2024年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes a robot vision system that detects illegal persons who violate house rules such as wearing shoes in a home environment. Such complex vision systems often require multiple AI systems including person detection, object detection, and more. Our approach simplifies this by leveraging the combined capabilities of Grounding DINO and SAM to detect rule violations effectively. The success of our method was proven at RoboCup@Home 2023, where it secured the highest score among all participating teams.

DOI： 10.5954/icarob.2024.os15-5

Development of A SayCan-based Task Planning System Capable of Handling Abstract Nouns 査読有り

Yamao Kosei, Kanaoka Daiju, Isomoto Kosei, Mizutani Akinobu, Tanaka Yuichiro, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 29 430 - 434 2024年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

The task planning system is required to accomplish various requests from a human in real-world environments. SayCan, one of the task planning systems, has high accuracy. However, its accuracy decreases for requests that include abstract nouns of the ambiguous word/phrase. We propose a novel task planning system based on SayCan that introduces a function for checking concrete names of abstract nouns and a rule-based skill extraction, enhancing accuracy. The proposed system facilitates the interpretation of requests and enables appropriate task planning. The effectiveness of the proposed system was demonstrated at RoboCup@Home, where it achieved high performance.

DOI： 10.5954/icarob.2024.os15-4

A Rapidly Adjustable Object Recognition System through Language Based Prompt Engineering 査読有り

Yamaguchi Naoki, Shiba Tomoya, Isomoto Kosei, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 29 425 - 429 2024年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

We propose the use of language-based prompt engineering to achieve rapidly adjustable object recognition in RoboCup@Home. The proposed prompt engineering involves humans adding features, such as the color and material of an object, into the text prompts inputted into Language Segment Anything. In this research, we evaluated the effectiveness of our proposed method in three benchmark tests for object recognition at RoboCup@Home 2023. The results show that the highest scores were obtained in specific tasks, indicating that the proposed method applies to various recognition tasks.

DOI： 10.5954/icarob.2024.os15-3

A Low Computational Cost Hand Waving Action Recognition System with Echo State Network for Home Service Robots 査読有り

Yamaguchi Hiromasa, Mizutani Akinobu, Syulistyo Arie Rachmad, Tanaka Yuichiro, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 29 421 - 424 2024年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This study proposes a low computational cost hand-waving action recognition system for non-verbal communication in home service robots. The system is based on an echo state network, which requires lower computational costs than that of deep neural networks (DNNs), and processes time-series data of skeletal coordinates of humans to recognize hand-waving actions. Additionally, this study proposes and compares two types of Preprocessing ing methods of the skeletal coordinates to ensure the robustness of the human positions on the frame: one method extracts shoulder and arm angles, which are invariable regardless of the humans' positions and the other normalizes the skeletal coordinates. The experimental result shows that the proposed system has competitive accuracy and is robust to varying human positions.

DOI： 10.5954/icarob.2024.os15-2

A hippocampus-inspired memory model with an accumulation function through place representation planes for digital VLSI implementation 国際誌

Akinobu Mizutani, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Osamu Nomura, and Takashi Morie

The 12th RIEC International Symposium on Brain Functions and Brain Computer (BFBC 2024) 2024年02月

Object recognition using CNT-PDMS nanocomposite sensor on a robot hand 査読有り国際誌

Shoshi Tokuno, Kouki Kimizuka, Yuichiro Tanaka, Hirofumi Tanaka, Hakaru Tamukoh

The 22nd International Symposium on Eco-materials Processing and Design (ISEPD2024) 2024年01月

YOLO real-time object detection on EV3-Robot using FPGA hardware Accelerator 査読有り

Pramanta D., Fuengfusin N., Syulistyo A.R., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 416 - 419 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

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極限環境に向けた占有格子地図に基づく走行可能性推定査読有り

福田有輝也, 三井悠也, 矢野優雅, 岩井秀成, 井上慎太郎, 田向権

自動車技術会論文集 ( 公益社団法人自動車技術会 ) 55 ( 4 ) 733 - 738 2024年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

自動運転において，走行可能性推定は車両周囲の危険性を定量化する技術であり，経路計画などで活用される． 走行可能性推定では車両周囲の環境把握が不可欠であるが，障害物の遮蔽などセンサの死角に対しては推定ができない． 本研究では，LiDARの点群を効果的に用いて死角の情報を補完し，走行可能性推定を実現する．

DOI： 10.11351/jsaeronbun.55.733

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001327

NaN Attacks: Bit-Flipping Deep Neural Network Parameters to NaN or Infinity 査読有り

Fuengfusin N., Tamukoh H.

2024 1st International Conference on Robotics, Engineering, Science, and Technology, RESTCON 2024 33 - 37 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Deep neural networks (DNN) have enabled various intelligent applications on computing devices, e.g., image recognition, voice recognition, and language modeling. When deploying DNNs in safety-critical applications, it is crucial to consider their vulnerabilities. For example, bit-flipping can cause DNNs to malfunction, and it can be induced through various means, e.g., hardware attacks, soft errors, or write errors in emerging memory devices. In this paper, we focus on subsets of bit-flipping outcomes of IEEE-754 32-bit floating point (FP32). These subsets are FP32 special numbers, i.e., not a number (NaN) and infinity (Inf). We found that performing 1-bit flips on subsets of parameters in DNN pretrained weights can produce NaN or Inf, thereby leading to model failure. Such NaN-sensitive and Inf-sensitive parameters were analyzed across 78 torchvision pretrained models. The results provide insight into their probable locations and ranges of magnitude. In addition, heuristic-based protection methods are proposed to mitigate such attacks.

DOI： 10.1109/RESTCON60981.2024.10463548

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RoboCup@Home 2023: Stickler for the Rules Task Solutions 査読有り

Shiba T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 435 - 437 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85190281037&origin=inward

Development of A SayCan-based Task Planning System Capable of Handling Abstract Nouns 査読有り

Yamao K., Kanaoka D., Isomoto K., Mizutani A., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 430 - 434 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85190283813&origin=inward

A Rapidly Adjustable Object Recognition System through Language Based Prompt Engineering 査読有り

Yamaguchi N., Shiba T., Isomoto K., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 425 - 429 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85190267450&origin=inward

A Low Computational Cost Hand Waving Action Recognition System with Echo State Network for Home Service Robots 査読有り

Yamaguchi H., Mizutani A., Syulistyo A.R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 421 - 424 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This study proposes a low computational cost hand-waving action recognition system for non-verbal communication in home service robots. The system is based on an echo state network, which requires lower computational costs than that of deep neural networks (DNNs), and processes time-series data of skeletal coordinates of humans to recognize hand-waving actions. Additionally, this study proposes and compares two types of Preprocessing ing methods of the skeletal coordinates to ensure the robustness of the human positions on the frame: one method extracts shoulder and arm angles, which are invariable regardless of the humans’ positions and the other normalizes the skeletal coordinates. The experimental result shows that the proposed system has competitive accuracy and is robust to varying human positions.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85190300153&origin=inward

Enhancing Memory Capacity of Reservoir Computing with Delayed Input and Efficient Hardware Implementation with Shift Registers 査読有り

Hirayae S., Yoshioka K., Yokota A., Kawashima I., Tanaka Y., Katori Y., Nomura O., Morie T., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

To use reservoir computing (RC) for practical tasks, both a high memory capacity and nonlinearity are required; however, some RC models have the problem of a low memory capacity. We propose a delay mechanism for increasing the memory capacity in RC as well as a simple and small-scale digital circuit for implementing the delay mechanism. The proposed delay mechanism is integrated into the input layer of the RC model and is expected to be implemented in several RC models, such as material reservoirs and chaotic Boltzmann machine (CBM)-RC. We conducted experiments using a CBM-RC with a delay mechanism (CBM-RC-DL) and evaluated the performance improvement achieved by introducing a delay mechanism. We used CBM-RC as the base model because it is an appropriate model for the hardware implementation of large networks but has a low memory capacity. The experimental results for CBM-RC-DL indicated that the delay mechanism significantly increased the memory capacity of CBM-RC with the addition of a small-scale circuit. Furthermore, the entire synthesized CBM-RC-DL was sufficiently small-scale to be implemented in a field-programmable gate array for edge computing, and it outperformed conventional methods in nonlinear autoregressive moving average 10 (NARMA10) - a benchmark task for time-series data processing. The proposed delay mechanism can facilitate the use of many RC models because of its simple structure.

DOI： 10.1109/ISCAS58744.2024.10557841

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85198500940&origin=inward

FPGA Implementation for Large Scale Reservoir Computing based on Chaotic Boltzmann Machine 査読有り

Matsumoto S., Ichikawa Y., Kajihara N., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This paper reports on a field programmable gate array (FPGA) implementation of Chaotic Boltzmann Machine Reservoir Computing (CBM-RC). The reservoir will be large-scale, as it is expected to be applied to sensor information prediction for autonomous mobile robots. Therefore, we employ a design premised on storing the weight information into a large memory outside the FPGA. We propose an efficient compression method for the weight matrix and a parallel processing system, by considering both the characteristics of CBM-RC and the fact that the weight matrix of a large-scale reservoir is generally a sparse matrix. Our RC system, which has more than 8000 neurons and 1024 inputs/outputs, has been implemented on an AMD Alveo U50 FPGA board. This RC is the largest scale compared to those in related studies. We have performed the NARMA10 task and demonstrated that we can estimate 1024 predictions at once with NMSE accuracy that is even or better to conventional RC.

DOI： 10.1109/ISCAS58744.2024.10558106

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85198518399&origin=inward

Unified Understanding of Environment, Task, and Human for Human-Robot Interaction in Real-World Environments 査読有り

Yano Y., Mizutani A., Fukuda Y., Kanaoka D., Ono T., Tamukoh H.

IEEE International Workshop on Robot and Human Communication, RO-MAN 224 - 230 2024年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

To facilitate human-robot interaction (HRI) tasks in real-world scenarios, service robots must adapt to dynamic environments and understand the required tasks while effectively communicating with humans. To accomplish HRI in practice, we propose a novel indoor dynamic map, task understanding system, and response generation system. The indoor dynamic map optimizes robot behavior by managing an occupancy grid map and dynamic information, such as furniture and humans, in separate layers. The task understanding system targets tasks that require multiple actions, such as serving ordered items. Task representations that predefine the flow of necessary actions are applied to achieve highly accurate understanding. The response generation system is executed in parallel with task understanding to facilitate smooth HRI by informing humans of the subsequent actions of the robot. In this study, we focused on waiter duties in a restaurant setting as a representative application of HRI in a dynamic environment. We developed an HRI system that could perform tasks such as serving food and cleaning up while communicating with customers. In experiments conducted in a simulated restaurant environment, the proposed HRI system successfully communicated with customers and served ordered food with 90% accuracy. In a questionnaire administered after the experiment, the HRI system of the robot received 4.2 points out of 5. These outcomes indicated the effectiveness of the proposed method and HRI system in executing waiter tasks in real-world environments.

DOI： 10.1109/RO-MAN60168.2024.10731235

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85209822695&origin=inward

Robust Binary Encoding for Ternary Neural Networks Toward Deployment on Emerging Memory 査読有り

Fuengfusin N., Tamukoh H., Nomura O., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2024年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Deep neural networks (DNNs) have enabled state-of-the-art performance across various applications. However, their deployment is often hindered by high energy demands. One solution is to deploy DNNs on hardware equipped with emerging non-volatile memory, which does not require energy to maintain memory state. Nonetheless, this approach may introduce bit-flips in DNN parameters, leading to a drop in model performance. To mitigate this issue, ternary neural networks (TNNs), which are known for their robustness against bit-flips, can be utilized. To accelerate TNNs, it is necessary to define binary representations (BRs) for ternary values to enable bit-wise operations. This paper proposes a framework for identifying a set of BRs that minimizes the discrepancy between the Top-1 Accuracy of TNNs before and after bit-flips, thereby enhancing their robustness. The BRs are evaluated using randomly generated data, the CIFAR-10 dataset, and the ImageNet 2012 dataset. The experimental results demonstrate that our BRs can significantly reduce or maintain the Top-1 Accuracy degradation caused by bit-flips, compared to conventional BRs.

DOI： 10.1109/IJCNN60899.2024.10650030

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85204983438&origin=inward

Object recognition and grasping point detection using carbon nanotube - polydimethylsiloxane nanocomposite sensor 査読有り

Tokuno Shoshi, Kimizuka Kouki, Tanaka Yuichiro, Usami Yuki, Tanaka Hirofumi, Tamukoh Hakaru

Nonlinear Theory and Its Applications, IEICE ( 一般社団法人電子情報通信学会 ) 15 ( 4 ) 883 - 898 2024年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper presents a system for processing tactile information using a carbon nanotube (CNT)-polydimethylsiloxane (PDMS) nanocomposite sensor designed for robotic applications. This study introduces an approach for recognizing objects and detecting optimal grasping points using tactile data from a sensor-equipped robotic hand. The sensor is expected to be more efficient than a computerized implementation using sensor dynamics directly in the computation. The experiments demonstrated the system's ability to classify nine types of objects with an accuracy of 83.56% and to discern two grasping points on four object types, achieving a 71.7% success rate.

DOI： 10.1587/nolta.15.883

その他リンク： https://www.jstage.jst.go.jp/article/nolta/15/4/15_883/_pdf

CMOS digital-analog mixed signal VLSI implementation of a hippocampus-inspired model 査読有り

Shishido Y., Nomura O., Tateno K., Tamukoh H., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2024年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

For artificial intelligence (AI) to be useful in the home, it is required to acquire unique knowledge of the home obtained through interaction with space and environment. This is difficult for deep learning-based AI. The human brain can learn unique knowledge from few experiences. The entorhinal cortex and hippocampus play essential roles for episodic memory formation and recall. While entorhinal-hippocampal models have been proposed that can reproduce episodic memory, hardware systems that implement such models face challenges related to high computational complexity, power consumption, and processing speed. In this paper, we propose a digital-analog mixed-signal CMOS VLSI implementation of a hippocampus-inspired model that can memorize and associate place and object information essential for the formation of episodic memory. By using both analog and digital in-memory computing architecture, the proposed circuit has achieved a computational efficiency of 22 TOPS/W, which is very high for AI hardware with a learning function. The proposed circuit was fabricated, measured, and evaluated. The results of an experiment using a fabricated chip and a control system showed that the proposed circuit can memorize and process place and object information, and can acquire environment-unique knowledge through interaction with a space.

DOI： 10.1109/IJCNN60899.2024.10650772

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85204948407&origin=inward

An efficient spatial representation scheme for memory-based hippocampus-inspired model with VLSI implementation 査読有り

Shishido Yuka, Nomura Osamu, Tateno Katsumi, Tamukoh Hakaru, Morie Takashi

Nonlinear Theory and Its Applications, IEICE ( 一般社団法人電子情報通信学会 ) 15 ( 4 ) 785 - 795 2024年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

Hippocampus integrates events and places information critical for episodic memory. Memory-based hippocampus-inspired model (MBHIM) is a hippocampus-inspired model suitable for VLSI implementation. This paper proposes an efficient spatial representation scheme for MBHIM with VLSI implementation. Each layer represents a space of a different size to represent and store spatial information at different resolutions. This scheme can achieve the representation of large spaces with high spatial resolutions, even when the number of memory cells per layer is limited. We conducted an experiment using a VLSI system of MBHIM to demonstrate the effectiveness of the proposed scheme.

DOI： 10.1587/nolta.15.785

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001317

A Hippocampus-Inspired Environment-Specific Knowledge Acquisition System Utilizing Common Knowledge with Contextual Information 査読有り

Mizutani A., Tanaka Y., Tamukoh H., Nomura O., Tateno K., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2024年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Home service robots acquire environment-specific knowledge through experiences (episodes) in a home to perform tasks autonomously. We propose a hippocampus-inspired memory system comprising an environment-specific knowledge module that handles episodic memory and a common knowledge module. The robot works in a home and needs to learn the locations of objects in the home with minimal user help to deliver or store objects. We employ a large language model (LLM) that runs on an edge device as the common knowledge module to protect the privacy of users. The environment-specific knowledge module acquires episodes and generates contextual information. The LLM receives contextual information generated by the environment-specific knowledge module, with which it infers an appropriate possible location. We verified that the LLM could infer the possible object locations and that the proposed system could minimize the required user effort.

DOI： 10.1109/IJCNN60899.2024.10651337

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85205013660&origin=inward

ホームサービスロボットのためのリザバーコンピューティングによる低計算コストの手振り認識システム

山口紘正，水谷彰伸，アリラフマードシュリスティヨ，田中悠一朗，田向権

第25回日本知能情報ファジィ学会九州支部学術講演会 2023年12月

曖昧な言語指示に対応可能な大規模言語モデルを用いた動作計画システムの開発査読有り

山尾晃世, 金岡大樹, 磯本航世, 田向権

人工知能学会第二種研究会資料 ( 一般社団法人人工知能学会 ) 2023 ( Challenge-063 ) 04 2023年11月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

人の言語指示から適切な動作を行う汎用的なサービスロボットの実現のためには，高精度な動作計画が必要となる．大規模言語モデルを活用したSayCanと呼ばれる動作計画システムは非常に高精度であるが，いくつかの問題を有している．我々は，SayCanの有する問題の中でも，具体的な対象を特定できない抽象名詞を含む曖昧な言語指示に対して，動作計画の精度が低下する問題と，システムが持つスキルの数に伴い推論時間が増加する問題に注目した．我々は，SayCanをベースとし，抽象名詞を聞き返す機能とルールベースでスキルを抽出する機能を導入した動作計画システムを提案する．提案システムは，言語指示の解釈を容易にし，適切な動作計画を可能とした．また，RoboCup@Homeと呼ばれるホームサービスロボットの性能を評価する競技会において高い成績を獲得することにより，実環境下でも十分動作することを示した．

DOI： 10.11517/jsaisigtwo.2023.challenge-063_04

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001130

曖昧な言語指示に対応可能な動作計画システムの開発

山尾晃世，金岡大樹，磯本航世，田向権

第63回人工知能学会 AIチャレンジ研究会，SIG-Challenge-063-04 2023年11月

Calculate a Destination for Communicating with a Customer in Unknown Environments 査読有り

Yuga Yano, Kazuma Sakakibara and Hakaru Tamukoh

11th International Symposium on Applied Engineering and Sciences 2023 (SAES2023) 2023年11月

Tuning an Object Recognition System Using Prompt Engineering in RoboCup@Home 査読有り

Naoki Yamaguchi, Tomoya Shiba, Kosei Isomoto, Hakaru Tamukoh

11th International Symposium on Applied Engineering and Sciences 2023 (SAES2023) 2023年11月

Verification of luminance detection circuit for FPGA implementation 査読有り

Ryuta Toyoda, Sansei Hori and Hakaru Tamukoh

11th International Symposium on Applied Engineering and Sciences 2023 (SAES2023) 2023年11月

Validation of Speech Recognition Using Data Recorded at RoboCup@Home 査読有り

Kosei Yamao, Yuga Yano, and Hakaru Tamukoh

11th International Symposium on Applied Engineering and Sciences 2023 (SAES2023) 2023年11月

Path Planning for Home Service Robots Using Semantic Map 査読有り

Kosei Isomoto, Yuga Yano, Yuichiro Tanaka and Hakaru Tamukoh

11th International Symposium on Applied Engineering and Sciences 2023 (SAES2023) 2023年11月

A Study on Parameter Optimization Based on Fireworks Algorithm for SLAM Algorithm 査読有り

Tomoaki Fujino, Hakaru Tamukoh, Akihiko Uchibori and Ryosuke Kubota

11th International Symposium on Applied Engineering and Sciences 2023 (SAES2023) 2023年11月

ManifoldNeRF: View-dependent Image Feature Supervision for Few-shot Neural Radiance Fields 査読有り

Daiju Kanaoka, Motoharu Sonogashira, Hakaru Tamukoh, Yasutomo Kawanishi

The 34th British Machine Vision Conference (BMVC2023) 2023年11月

カオス・ボルツマン・マシンをべースとしたレザバーコンピューティングの高位合成実装

松本茂樹，市川湧希，梶原信樹，田向権

電子情報通信学会リコンフィギャラブルシステム研究会(RECONF) vol.123 ( 261 ) 231 - 236 2023年11月

Simulator-based Dataset Generator for Pothole Detection in Autonomous Driving 査読有り

Yuga Yano, Yukiya Fukuda and Hakaru Tamukoh

11th International Conference on Informatics, Electronics & Vision (ICIEV) 2023年10月

A Tri-modal Fusion Network for Object Detection Using Small Amounts of Low-Quality Data 査読有り

Yusuke Watanabe, Yuma Yoshimoto and Hakaru Tamukoh

11th International Conference on Informatics, Electronics & Vision (ICIEV) 2023年10月

大規模言語モデルと海馬モデルによるホームサービスロボット向け知識獲得システム

水谷彰伸，田中悠一朗，田向権，立野勝巳，野村修，森江隆

電子情報通信学会スマートインフォメディアシステム研究会（SIS） 2023年10月

Reservoir Reinforcement Learning with Chaotic Boltzmann Machines Implemented on an FPGA 査読有り

Sakino Yamato, Tamukoh Hakaru, Morie Takashi, Katori Yuichi

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 76 422 - 425 2023年09月

記述言語：英語掲載種別：研究論文（学術雑誌）

Reinforcement learning has led to advances in areas such as attitude control in robotics and action planning in autonomous driving systems. However, reinforcement learning algorithms often face computational bottlenecks that limit their application to edge devices. In recent years, reservoir computing has emerged as a potential solution to this problem. One of these models updates the weight matrix using Q-learning, a popular reinforcement learning algorithm. This paper introduces reservoir reinforcement learning using chaotic Boltzmann machines on a field programmable gate array. We demonstrate the efficient implementation of reservoir computing with chaotic Boltzmann machines in hardware, achieving low computational costs. To demonstrate the effectiveness of this approach, we validate the proposed model using an action planning task in a two-dimensional environment consisting of nine rooms. The result shows that the model can be implemented in digital circuits with low power consumption and hardware resource savings while maintaining problem-solving capabilities. This result suggests efficient machine learning hardware in action planning could be applied to real-world scenarios.

DOI： 10.34385/proc.76.c1l-33

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001134

Ensemble Learning of Multiple Readouts for Reservoir Computing 査読有り

Tanaka Yuichiro, Tamukoh Hakaru

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 76 509 - 512 2023年09月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This study proposes a reservoir computing model with multiple readouts and an associated training method to enhance the training capability of reservoir computing. This study conducts a speaker classification task and a word classification task using an audio dataset consisting of digits pronounced by six persons. The experimental results reveal that the proposed model with multiple readouts outperforms a conventional model with a single readout.

DOI： 10.34385/proc.76.c3l-11

その他リンク： https://kyutech.repo.nii.ac.jp/records/2000241

he Reproduction of Calcium Ion Response in C. elegans Olfactory Neurons using Echo State Networks

高島康介，石橋涼輔，中西慶一，田中悠一朗，田向権，佐藤則子，石原健，徳永旭将

The Reproduction of Calcium Ion Response in C. elegans Olfactory Neurons using Echo State Networks，" 2023年日本バイオインフォマティクス学会年会第12回生命医薬情報学連合大会 2023年09月

The application of Echo State Network in reproducing the membrane potential response of olfactory neurons in C. elegans

石橋涼輔，高島康介，中西慶一，田向権，田中悠一朗，石原健，佐藤則子，徳永旭将

2023年日本バイオインフォマティクス学会年会第12回生命医薬情報学連合大会 2023年09月

Optimizing a Field-Programmable Gate Array Object Detection System Considering Processing System and Programmable Logic Load Balance 査読有り

Watanabe Y., Tamukoh H.

Journal of Robotics, Networking and Artificial Life ( 株式会社 ALife Robotics ) 10 ( 2 ) 105 - 114 2023年09月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

A field-programmable gate array (FPGA) device with a Zynq architecture integrates a processing system (PS) and programmable logic (PL) into a single chip. Although the PL performance is typically considered, the PS load cannot be completely ignored. In this study, using an FPGA board with a Zynq architecture, the conditions under which an object detection system performs the best, while considering the PS and PL load balance, are explored.

DOI： 10.57417/jrnal.10.2_105

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85196811480&origin=inward

Robust Trash Can Lid Opening System 査読有り国際誌

Kosei Isomoto, Yuga Yano, Yuichiro Tanaka and Hakaru Tamukoh

2023 International Workshop on Smart Info-Media Systems in Asia (SISA2023) 2023年08月

ManifoldNeRF: 視点変化に伴う特徴ベクトルの連続変化に着目したFew-shot NeRF

金岡大樹，薗頭元春，田向権，川西康友

画像の認識・理解シンポジウム（MIRU2023） 2023年07月

Autonomous Waiter Robot System for Recognizing Customers, Taking Orders, and Serving Food 査読有り

Yuga Yano, Kosei Isomoto,Tomohiro Ono, Hakaru Tamukoh

RoboCup 2023: Robot World Cup XXVI 252 - 261 2023年07月

海馬機能を模倣したディジタル・アナログ併用脳型メモリ回路コア査読有り

宍戸優樺 , 野村修立野勝巳 , 田向権 , 森江隆

LSIとシステムのワークショップ2023 2023年05月

未知視点に対する疑似特徴ベクトル評価によるFew-shot NeRFの検討

金岡大樹，薗頭元春，田向権，川西康友

電子情報通信学会パターン認識・メディア理解研究会（PRMU） 2023年03月

DnCNNを用いた生成AIに対する画像作品保護

福田有輝也*，金岡大樹*，田向権

電子情報通信学会パターン認識・メディア理解研究会（PRMU） 2023年03月

未知視点に対する疑似特徴ベクトル評価によるFew-shot NeRFの検討

金岡大樹，薗頭元春，田向権，川西康友

研究報告コンピュータビジョンとイメージメディア（CVIM） 2023-CVIM-233 ( 55 ) 1 - 6 2023年02月

記述言語：英語掲載種別：研究論文（学術雑誌）

任意視点画像生成において Neural Radiance Fields は非常に強力な手法ではあるが，学習のためには多くの画像が必要である．本発表では，パラメトリック固有空間法のアナロジーとして，教師データが存在しない未知視点において，近傍視点の画像から擬似的に生成した特徴ベクトルを教師信号として学習に用いることで，少数の画像でも学習できる Manifold NeRF を提案する．実験の結果，疑似教師信号を用いることで，データセットが少量でも精度良く任意視点画像が生成できることを確認した．

Robust Classification Model with Multimodal Learning for Home Service Robots 査読有り

Matsumoto Ikuya, Kanaoka Daiju, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 28 404 - 408 2023年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

We propose an auxiliary data stream structure as a robust classification model. The model treats one modal as the main input and the other modals as supports. The model chooses how much of the sub-modal is used for classification. We experimented with two and three modal inputs. Moreover, we added pseudo-shadows to the visual information for the experiment with three modal inputs. In all experiments, our proposed model improves the accuracy and robustness to environmental disturbances by using multiple modals.

DOI： 10.5954/icarob.2023.os17-3

その他リンク： https://kyutech.repo.nii.ac.jp/records/7917

Pose Detection for Flexible-Indefinite Objects using Pseudo-Bone Data 査読有り

Yoshimoto Yuma, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 28 397 - 399 2023年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes a method for recognizing the poses of a flexible-indefinite object. Some flexible-indefinite objects, such as fried shrimp, differ between individuals. Therefore, it is difficult to estimate the pose of these foods by point cloud fitting or other methods. We propose "pseudo-skeleton" data for these objects. Pseudo-skeleton data consists of "key-points," which are joints, and "bones," which connect between key-points. For example, fried shrimp are given 3 key-points; "head," "belly," and "tail." In addition, the bones that connect them are given. In the experiment, objects pose recognition model based on the human pose recognition model trains pseudo-skeleton data of fried shrimp. We confirmed that the model estimates the poses in the images.

DOI： 10.5954/icarob.2023.os17-1

その他リンク： https://kyutech.repo.nii.ac.jp/records/7915

Object Search and Empty Space Detection System for Home Service Robot 査読有り

Shiba Tomoya, Ono Tomohiro, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 28 400 - 403 2023年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Home service robots have pick-and-place tasks to grasp and carry objects. When performing pick-andplace tasks, robots need to process to search for objects in the case of missing a target object. We propose approaches for the robot to search for a target object from a shelf and a method for selecting empty spaces to move off-target objects. The proposed method is based on two recognition models to select a space and plan motions. The object is to be moved and the space is selected based on the size and position of empty spaces. In the experiments, the robot planned actions to search for a target object from a shelf and to place an object near a group of similar objects. We used these in the RoboCup@Home competition to evaluate the effectiveness of the proposed method.

DOI： 10.5954/icarob.2023.os17-2

その他リンク： https://kyutech.repo.nii.ac.jp/records/7916

Impact of PS Load on FPGA Object Detection System Performance 査読有り

Watanabe Yusuke, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 28 415 - 419 2023年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

A field-programmable gate array (FPGA) device which has a Zynq architecture becomes popular these days. It is featured by integration of processing system (PS) and programmable logic (PL) into a single chip. While we tend to focus on the performance of PL, we can not ignore PS load completely. In this paper, using a Zynq FPGA board, we explore how our object detection system performance changes with PS load and report our experiment results.

DOI： 10.5954/icarob.2023.os17-5

その他リンク： https://kyutech.repo.nii.ac.jp/records/7912

An Effective Method for Minimizing Domain Gap in Sim2Real Object Recognition Using Domain Randomization 査読有り

Ono Tomohiro, Suzuki Akihiro, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 28 420 - 424 2023年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Manual annotation is common, but problems occur, such as oversight and mislabeling via human error. These problems are known to affect the quality of datasets significantly. To resolve these problems, we propose a method to automatically generate high-quality and large datasets in a short time using a simulator. Our proposed method uses domain randomization to minimize domain gaps without faithfully reproducing real scenes. The generated dataset achieved more than 80% recognition accuracy against the real image dataset.

DOI： 10.5954/icarob.2023.os17-6

その他リンク： https://kyutech.repo.nii.ac.jp/records/7911

Flexible Human-Robot Interaction in Domestic Environment Using Semantic Map 査読有り

Yano Yuga, Fukuda Yukiya, Ono Tomohiro, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 28 409 - 414 2023年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

We propose an efficient semantic map to realize flexible human-robot interaction (HRI) in domestic environments. Our proposed map is created from an output of Simultaneous Localization and Mapping and already-known environmental information such as furniture and room. In this study, we evaluated the effectiveness of our proposed method on two benchmark tests for HRI in RoboCup@Home held in Bangkok in 2022. In the RoboCup@Home, we employ 3D human recognition to apply our proposed map to HRI, such as "find and offer an empty seat." Our proposed method had the best score of all teams on both tests. The results of our experiments are available at https://youtube.com/playlist?list=PLfbN50Mwh2DG3OPDeCHo4TNuyrU4qYCrJ.

DOI： 10.5954/icarob.2023.os17-4

その他リンク： https://kyutech.repo.nii.ac.jp/records/7918

Multiple memory accumulation and recall by a hippocampus model reflecting emotional values 査読有り国際誌

Akinobu Mizutani, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Osamu Nomura, Takashi Morie

The 11th RIEC International Symposium on Brain Functions and Brain Computer 2023年02月

未知視点に対する疑似特徴ベクトル評価によるFew-shot NeRFの検討国際誌

金岡大樹，薗頭元春，田向権，川西康友

研究報告コンピュータビジョンとイメージメディア（CVIM )電子情報通信学会パターン認識・メディア理解研究会（PRMU） ( 電子情報通信学会パターン認識・メディア理解研究会（PRMU） ) 55 ( 1 ) 6 2023年02月

記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）

Pose Detection for Flexible-Indefinite Objects using Pseudo-Bone Data 査読有り国際誌

Yuma Yoshimoto and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023) 2023年02月

Object Search and Empty Space Detection System for Home Service Robot 査読有り国際誌

Tomoya Shiba, Tomohiro Ono and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023) 2023年02月

Robust Classification Model with Multimodal Learning for Home Service Robots 査読有り国際誌

Ikuya Matsumoto, Daiju Kanaoka and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023) 2023年02月

Flexible Human-Robot Interaction in Domestic Environment Using Semantic Map 査読有り国際誌

Yuga Yano, Yukiya Fukuda, Tomohiro Ono, and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023) 2023年02月

Impact of PS load on FPGA object detection system performance 査読有り国際誌

Yusuke Watanabe and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023) 2023年02月

An Effective Method for Minimizing Domain Gap in Sim2Real Object Recognition Using Domain Randomization 査読有り国際誌

Tomohiro Ono, Akihiro Suzuki and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023) 2023年02月

Traffic Flow Optimization using a Chaotic Boltzmann Machine Annealer on an FPGA 査読有り

Yoshioka K., Tanaka Y., Tamukoh H.

Proceedings - International Conference on Field-Programmable Technology, ICFPT 266 - 267 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

We demonstrate a chaotic Boltzmann machine annealer, which is a field-programmable gate array (FPGA)-based high-performance annealing machine, and a control system powered by two open-source software packages. The annealer is employed in solving a large-scale real-world optimization problem, the traffic flow optimization. This involves distributing 500 car routes between the Haneda Airport area to the Pacifico Yokohama area, the venue of the International Conference on Field Programmable Technology 2023, while minimizing the total driving distance and preventing traffic jams. We obtain solutions that are comparable in accuracy to solutions of simulated annealing running on a graphics processing unit (GPU-SA) and a central processing unit (CPU-SA). The annealing machine on an FPGA is approximately 571 and 97600 times as fast as the GPUSA and the CPU-SA, respectively. We will demonstrate solving traffic flow optimization in the towns familiar to the visitors.

DOI： 10.1109/ICFPT59805.2023.00038

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85187557051&origin=inward

In-material reservoir implementation of reservoir-based convolution 査読有り

Tanaka Y., Usami Y., Tanaka H., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2023-May 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This study aims to implement a reservoir-based convolutional neural network (CNN) on physical reservoir computing (RC) to develop an efficient image recognition system for edge AI. Therefore, we propose a novel reservoir-based convolution circuit system that uses in-material reservoir computing, a type of physical RC made from a sulfonated polyaniline network. The experimental results demonstrate that the proposed circuit system extracts image features in the same way as the original CNN and that a reservoir-based CNN on the in-material RC achieves an accuracy rate of 81.7% in an image classification task while an echo state network-based CNN achieves 87.7%.

DOI： 10.1109/ISCAS46773.2023.10181436

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85167720431&origin=inward

In-Material Reservoir Implementation of reservoir-Based Convolution 査読有り国際誌

Yuichiro Tanaka, Yuki Usami, Hirofumi Tanaka Hakaru Tamukoh

IEEE International Symposium on Circuits and Systems (ISCAS2023) 2023年01月

ホームサービスロボットのための高精度な特定人物追跡システムの開発査読有り

小野智寛, 阿部佑志, 石田裕太郎, 柴田智広, 田向権

日本ロボット学会誌 ( 一般社団法人日本ロボット学会 ) 41 ( 2 ) 198 - 201 2023年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

In this paper, we propose a highly accurate specific person tracking system for home service robots. This study focuses on Selected Online Ada-Boosting (SOAB) and Kernelized Correlation Filters (KCF). It proposes the Selected KCF (SKCF), which combines features of fast adaptation to a specific person and robustness for occlusion. We also propose a system that combines You Only Look Once (YOLO), a real-time object detection algorithm, with SKCF to initialize and recover the tracking system. The proposed system runs at 17 [fps] in an experimental environment with an Intel Core i7-8700K CPU and an NVIDIA GTX 1080 GPU. We also show that the tracking accuracy of the proposed system on public datasets for tracking specific persons is better than that of conventional methods.

DOI： 10.7210/jrsj.41.198

その他リンク： https://www.jstage.jst.go.jp/article/jrsj/41/2/41_41_198/_pdf

Robust Classification Model with Multimodal Learning for Home Service Robots 査読有り

Matsumoto I., Kanaoka D., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 407 - 412 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149480798&origin=inward

Self-Organizing Multiple Readouts for Reservoir Computing 査読有り

Tanaka Y., Tamukoh H.

IEEE Access 11 138839 - 138849 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

With advancements in deep learning (DL), artificial intelligence (AI) technology has become an indispensable tool. However, the application of DL incurs significant computational costs, making it less viable for edge AI scenarios. Consequently, the demand for cost-effective AI solutions, other than DL-based approaches, is increasing. Reservoir computing (RC) has attracted interest owing to its ability to provide low-cost training alternatives, holding great promise for edge AI applications. However, the training capability of RC is constrained by its reliance on a single linear layer, while weight connections in the remaining layers remain static during training. Moreover, accomplishing continuous learning tasks is difficult owing to the catastrophic forgetting in the linear layer. Therefore, we propose the integration of self-organizing multiple readouts to enhance RC's training capability. Our method distributes training data across multiple readouts, which prevents catastrophic forgetting of readouts and empowers each readout to adeptly assimilate new data, thereby elevating the overall training performance. The self-organizing function, which assigns similar data to the same readout, optimizes the memory utilization of these multiple readouts. Experimental results show that an RC equipped with the proposed multiple readouts successfully solved a continuous learning task by mitigating catastrophic forgetting because of the data distribution to the multiple readouts. Additionally, the RC achieved higher accuracy in a sound recognition task compared with the existing RC paradigm because of ensemble learning in the multiple readouts. Multiple readouts are effective in enhancing the training capability of RC and can contribute to the realization of RC applications.

DOI： 10.1109/ACCESS.2023.3340311

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179802812&origin=inward

Reservoir-based 1D convolution: low-training-cost AI 査読有り国際誌

TANAKA Yuichiro, TAMUKOH Hakaru

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences ( 一般社団法人電子情報通信学会 ) advpub ( 0 ) 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

In this study, we introduce a reservoir-based one-dimensional (1D) convolutional neural network that processes time-series data at a low computational cost, and investigate its performance and training time. Experimental results show that the proposed network consumes lower training computational costs and that it outperforms the conventional reservoir computing in a sound-classification task.

DOI： 10.1587/transfun.2023eal2050

その他リンク： https://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2023EAL2050/_pdf

Random network device fabricated using Ag<inf>2</inf>Se nanowires for data augmentation with binarized convolutional neural network 査読有り

Kotooka T., Tanaka Y., Tamukoh H., Usami Y., Tanaka H.

Applied Physics Express 16 ( 1 ) 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

An Ag2Se nanowire random network was fabricated for application as a data augmentation device and combined with a binary convolutional neural network (BCNN) to achieve high accuracy in voice classification tasks. Due to the nonlinear high-dimensional characteristics resulting from the formation of the conductive filament at the cross junction, the Ag2Se device could transform input data into higher-order multiple signals, thereby enhancing the accuracy of the classification task by augmenting input signals. The results indicate that materials can realize data augmentation with the same performance as software, suggesting that material-based hardware can be used as an elemental technology for information processing.

DOI： 10.35848/1882-0786/acae6a

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85146494177&origin=inward

Object Search and Empty Space Detection System for Home Service Robot 査読有り

Shiba T., Tomohiro O., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 402 - 406 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Home service robots have pick-and-place tasks to grasp and carry objects. When performing pick-and-place tasks, robots need to process to search for objects in the case of missing a target object. We propose approaches for the robot to search for a target object from a shelf and a method for selecting empty spaces to move off-target objects. The proposed method is based on two recognition models to select a space and plan motions. The object is to be moved and the space is selected based on the size and position of empty spaces. In the experiments, the robot planned actions to search for a target object from a shelf and to place an object near a group of similar objects. We used these in the RoboCup@Home competition to evaluate the effectiveness of the proposed method.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149480150&origin=inward

Pose Detection for Flexible-Indefinite Objects using Pseudo-Bone Data 査読有り

Yoshimoto Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 399 - 401 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149467263&origin=inward

Efficient Repetition Coding for Deep Learning Towards Implementation Using Emerging Non-Volatile Memory with Write-Errors 査読有り

Fuengfusin N., Tamukoh H., Tanaka Y., Nomura O., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2023-June 2023年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Emerging non-volatile memory devices, such as resistive random access memory (ReRAM) and voltage-controlled magnetoresistive random access memory (VC-MRAM), promise low energy consumption for artificial intelligence applications. However, when implementing deep neural networks (DNNs) using such memory devices, write-error may cause millions of bit-flipping to DNN. This easily degrades the DNN performance. To address this problem, we propose a novel repetition coding for deep-learning (RC-DL), which is a repetition coding designed to protect IEEE 32-bit floating-point (FP32) DNN models. Compared to conventional repetition coding, the proposed RC-DL exploits FP32 non-uniform magnitude encoding by increasing the repeat rates to protect sensitive bit positions and reduce the repeat rates to insensitive bit positions. Hence, RC-DL uses a number of bits equivalent to a 3-bit repetition code while delivering the performance close to 11-bit repetition code. We perform extensive Monte Carlo simulations to simulate the write-error property with ImageNet 2012 pretrained models. The DNN models with RC-DL are shown to be operable in the extremely imperfect environment while delivering with only minor reductions in DNN performance.

DOI： 10.1109/IJCNN54540.2023.10191433

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Dense Traversability Estimation System for Extreme Environments 査読有り

Fukuda Y., Mii Y., Yano Y., Iwai H., Inoue S., Tamukoh H.

IEEE Intelligent Vehicles Symposium, Proceedings 2023-June 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Traversability estimation is essential for safe path planning in robotics and autonomous driving. In this study, we propose an accurate and dense traversability estimation system for extreme environments such as disaster areas. Traversability estimations often occur undefined regions due to the shielding and sparsity of sensor data. These regions may lead to the selecting of dangerous paths. The proposed system uses point cloud accumulation and the Bayesian generalized kernel (BGK) elevation estimation to create a dense Digital elevation map (DEM) with no undefined regions from point clouds obtained from light detection and ranging (LiDAR). Subsequently, traversability is estimated from road surface roughness, slope and vehicle performance using fuzzy logic in our system. In our experiments, our system is installed in an experimental vehicle, and the experiments conducted on rocky terrain, slopes, and craters to verify its effectivity in extreme environments. Results show that our system can create a dense DEM with few undefined regions and estimate valid traversability for all obstacles.

DOI： 10.1109/IV55152.2023.10186556

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85168006996&origin=inward

Memory-Efficient Implementation of GMM-MRCoHOG for HumaRecognition Hardware 査読有り

Takemoto R., Nagamine Y., Yoshihiro K., Shibata M., Yamada H., Tanaka Y., Enokida S., Tamukoh H.

Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 4 648 - 655 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

High-speed and accurate human recognition is necessary to realize safe autonomous mobile robots. Recently, human recognition methods based on deep learning have been studied extensively. However, these methods consume large amounts of power. Therefore, this study focuses on the Gaussian mixture model of multiresolution co-occurrence histograms of oriented gradients (GMM-MRCoHOG), which is a feature extraction method for human recognition that entails lower computational costs compared to deep learning-based methods, and aims to implement its hardware for high-speed, high-accuracy, and low-power human recognition. A digital hardware implementation method of GMM-MRCoHOG has been proposed. However, the method requires numerous look-up tables (LUTs) to store state spaces of GMM-MRCoHOG, thereby impeding the realization of human recognition systems. This study proposes a LUT reduction method to overcome this drawback by standardizing basis function arrangements of Gaussian mixture distributions in GMM-MRCoHOG. Experimental results show that the proposed method is as accurate as the previous method, and the memory required for state spaces consuming LUTs can be reduced to 1/504th of that required in the previous method.

DOI： 10.5220/0011698400003417

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85183596965&origin=inward

LUTNet-RC: Look-Up Tables Networks for Reservoir Computing on an FPGA 査読有り

Yoshioka K., Tanaka Y., Tamukoh H.

Proceedings - International Conference on Field-Programmable Technology, ICFPT 170 - 178 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

We propose look-up tables networks-based reservoir computing (LUTNet-RC). This work is the first trial of applying LUTNets to RC. LUTNet-RC consists of a LUT-based reservoir layer and a non-LUT-based output layer. LUTNets have disadvantages such as limited sparse connectivity and weights cannot be changed after implementation. However, when applied to a reservoir layer of RC (LUT-based reservoir layer), these disadvantages are eliminated, because this layer works with sparse connectivity and the weights are fixed, so only the advantage of small circuit resources is obtained. For the LUT-based reservoir layer, we propose and model a multi-bit weight reservoir, modifying the conventional binarized reservoir to improve calculation accuracy. In the case of LUTNets, the proposed multi-bit weight reservoir can be implemented without the increase in utilized circuit resources because LUTNets focus only on the input-output relationship on neurons. Additionally, we propose a speed-up method in the output layer with time division calculation, which compares the current network state with previous states and then calculates only status-changed neurons. As a result, we implement a LUTNet-RC with 1500 reservoir neurons on a field-programmable gate array (KR260) running at 100MHz. The utilized circuit resources are dominated by LUTs, which use approximately 26% of the total amount of LUTs. The LUTNet-RC can infer more than 106 data per second. We also verify the LUTNet-RC performance using nonlinear auto-regressive moving average 10 (NARMA10) and the performance is comparable to conventional works. We conclude that the LUTNet-RC is one of the highest-performance RC on an FPGA.

DOI： 10.1109/ICFPT59805.2023.00024

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85187557709&origin=inward

Impact of PS Load on FPGA Object Detection System Performance 査読有り

Watanabe Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 419 - 423 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149463385&origin=inward

Flexible Human-Robot Interaction in Domestic Environment Using Semantic Map 査読有り

Yano Y., Fukuda Y., Ono T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 413 - 418 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149485763&origin=inward

ホームサービスロボットのための高精度な特定人物追跡システムの開発査読有り

小野智寛, 阿部佑志, 石田裕太郎, 柴田智広, 田向権

日本ロボット学会誌 41 ( 2 ) 198 - 201 2023年01月

An Effective Method for Minimizing Domain Gap in Sim2Real Object Recognition Using Domain Randomization 査読有り

Ono T., Suzuki A., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 424 - 428 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149469686&origin=inward

FPGA Implementation of a Chaotic Boltzmann Machine Annealer 査読有り

Yoshioka K., Katori Y., Tanaka Y., Nomura O., Morie T., Tamukoh H.

Proceedings of the International Joint Conference on Neural Networks 2023-June 2023年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Ising machines are attracting attention for their ability to solve large-scale combinatorial optimization problems because these problems are difficult to solve. To accelerate the computing of Ising machines, implementation of Ising machines with digital circuits such as simulated annealing (SA) machines is in progress. However, these Ising machines on digital circuits require random number generators, which are implemented with large circuit resources. This work focuses on chaotic Boltzmann machines (CBMs), which imitate the stochastic behavior of Boltzmann machines (BMs) with deterministic chaotic dynamics. CBMs are one of the models that work as chaotic simulated annealing (CSA) machines within Ising machines. Therefore, we can implement the Ising machines without random number generators by using CBMs. In conventional work, CSA machines using CBMs (CBM-CSAs) are implemented with some hardware-oriented algorithms, but the CBM-CSA circuit is not optimized for these hardware-oriented algorithms. In the conventional CBM-CSA circuit, memory circuits are implemented separately, which prevents making the CBM-CSA from larger, and neuron circuits require the reset of accumulated values, which causes the increase in the calculation time. To solve these problems, we implement only one large memory circuit to make the CBM-CSA larger and improve the neuron circuits to allow dynamic changes of inputs to arithmetic circuits to inhibit the increase in the calculation time. As a result, we implement a CBM-CSA with 4096 nodes on an FPGA (Alveo U250), and the CBM-CSA can control 16-bit width weights and run at 100MHz. We evaluate the implemented CBM-CSA by solving K4000, max-cut problem, which is one of the combinatorial optimization problems. The best solution of CBM-CSA is comparable to that of the SA on the central processing unit (CPU). Moreover, the CBM-CSA is approximately 600 times as fast as the SA on the CPU and approximately twice as fast as the conventional Ising machine on an FPGA based on the improvements in this work. Furthermore, this work implements one of the highest-performance Ising machines on a single FPGA.

DOI： 10.1109/IJCNN54540.2023.10191342

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85169581320&origin=inward

Proposal of Robust Multimodal Learning Model for Object Classification 査読有り

Ikuya Matsumoto, Daiju Kanaoka and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware P1-13 2022年12月

A New ORB Descriptor Using RGB Channels 査読有り

Yuga Yano, Yukiya Fukuda, Tomohiro Ono and Hakaru Tamukoh

10th International Symposium on Applied Engineering and Sciences (SAES2022) 2022年12月

Binarized Dual Stream Convolutional Neural Network Implemented in an FPGA 査読有り

Ninnart Fuengfusin, Dinda Pramanda and Hakaru Tamukoh

10th International Symposium on Applied Engineering and Sciences (SAES2022) 2022年12月

Reservoir-based 1D convolution 査読有り

Yuichiro Tanaka and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 2022年12月

Toward Low-Power Intelligent Embedded Systems for Home Service Robots: FPGA Implementation of Binarized Dual-Stream CNN 査読有り

Yuma Yoshimoto, and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 2022年12月

Embedding Binary Neural Network to Floating-Point Neural Network 査読有り

Ninnart Fuengfusin, Dinda Pramanda and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 2022年12月

Resorvoir-based neuron model to emulate cellular neural activity responding to odor in C. elegans 査読有り

Ryosuke Ishibashi, Takumi Nakamura, Noriko Sato, Takeshi Ishihara, Yuichiro Tanaka, Hakaru Tamukoh, and Terumasa Tokunaga

The 4th International Symposium on Neuromorphic AI Hardware P2-6 2022年12月

Multi Object Tracking using SORT with Simple Re-Identification, 査読有り

Wei-Ding Wang, Ninnart Fuengfusin, Danny Wee-Kiat Ng, Ban-Hoe Kwan and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware P1-16 2022年12月

Object Detection with Voice Feedback for Blind Assistance 査読有り

Benjy Wei-Xiang Yeoh, Ninnart Fuengfusin, Danny Wee-Kiat Ng, Ban-Hoe Kwan and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware P1-14 2022年12月

Classification of Materials withTactile Properties using Stacking Ensemble 査読有り

Michelle Tze-Xin Loo, Ninnart Fuengfusin, Kouki Kimizuka, Saman Azhari, Danny Wee-Kiat Ng, Ban-Hoe Kwan, Yuki Usami, Hirofumi Tanaka, and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware P1-14 2022年12月

Robust Trash Can Lid Opening System 査読有り

Kosei Isomoto, Yuga Yano, Yuichiro Tanaka and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware P2-14 2022年12月

Deep Learning Method on Word Level American Sign Language Recognition from Video 査読有り

Arie Rachmad Syulistyo, Yuichiro Tanaka and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware P1-12 2022年12月

Evaluation of Modular Reservoirs Using Chaotic Boltzmann Machines 査読有り

Kazuo Nakahara, Yuichi Katori, Osamu Nomura, Hakaru Tamukoh, and Takashi Morie

The 4th International Symposium on Neuromorphic AI Hardware P2-8 2022年12月

PerformanceEvaluation of a Reservoir Reinforcement Learning Model Considering Nonlinear Write Characteristics of Analog Memory 査読有り

Katsunori Tamai, Yuichi Katori, Hakaru Tamukoh, Osamu Nomura, and Takashi Morie

The 4th International Symposium on Neuromorphic AI Hardware P2-7 2022年12月

CMOS VLSI implementation of a hippocampal conjunctive place-cue cells network 査読有り

Yuka Shishido, Osamu Nomura, Katsumi Tateno, Hakaru Tamukoh, and Takashi Morie

The 4th International Symposium on Neuromorphic AI Hardware P2-6 2022年12月

A Visualization System of Personal Characteristics for Human-Robot Interaction in RoboCup@Home 査読有り

Yuga Yano, Ikuya Matsumoto, Yukiya Fukuda, Tomohiro Ono and Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware P2-13 2022年12月

Hardware-oriented Brain-inspired Model with Memory Accumulation and Recall Functions to Generate Actions of Home Service Robots 査読有り

Akinobu Mizutani, Ichiro Kawashima, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Osamu Nomura and Takashi Morie

The 4th International Symposium on Neuromorphic AI Hardware P2-12 2022年12月

Robust Object Recognition for Home Service Robot with Multimodal Learning 査読有り

Ikuya Matsumoto, Daiju Kanaoka and Hakaru Tamukoh

10th International Symposium on Applied Engineering and Sciences (SAES2022) 2022年12月

RoboCup＠Homeのヒューマンインタラクションタスクに向けた解法の提案査読有り

矢野優雅, 松本生弥, 福田有輝也, 小野智寛, 田向権

人工知能学会第二種研究会資料 ( 一般社団法人人工知能学会 ) 2022 ( Challenge-060 ) 07 - 11 2022年10月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

ホームサービスロボットの技術発展を目的として，RoboCup@Homeという競技会が開催されている．RoboCup@Homeでは，実際の家庭環境を模したフィールドを用いてタスクを行うことで，より現実に近い環境でロボットの性能を評価することができる．本研究では，RoboCup@HomeのタスクであるFind My Matesを解くために，人物認識や音声認識を用いた動的環境でも動作する手法を提案する．また，提案した手法をロボットに実装し，2022年7月にバンコクで行われたRoboCup@Homeにて性能評価を行った．競技会では満点を獲得し，提案手法の有効性を示した．競技中の様子は，https://www.youtube.com/watch?v=ucoP8_j6Kigにて公開している．

DOI： 10.11517/jsaisigtwo.2022.challenge-060_07

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001149

Reservoir computing in the matter based on electrochemical dynamics 査読有り国際誌

Usami Y, Tanaka Y, Tamukoh H, Matsumoto T, van der Wiel W, Tanaka H

16th International Conference on Nanostructured Materials (NANO2022) P-PM13 2022年06月

Reservoir-based convolution 査読有り国際誌

Yuichiro Tanaka, Hakaru Tamukoh

Nonlinear Theory and Its Applications, IEICE (NOLTA) E13-N ( 2 ) 2022年04月

Object Position Estimation by Primitive Fitting with Point Clouds for Bulk Picking 査読有り

Yuma Yoshimoto, Hakaru Tamukoh

The 3rd International Symposium on Neuromorphic AI Hardware P-PM13 2022年03月

Memory-based Action Planning Inspired by Hippocampal Replay 査読有り

Akinobu Mizutani, Yuichiro Tanaka, Ichiro Kawashima, Hakaru Tamukoh, Katsumi Tateno, Takashi Morie

The 3rd International Symposium on Neuromorphic AI Hardware Oral-4, 2022年03月

A Memory-based LSI Architecture for Entorhinal-hippocampal Model 査読有り

Osamu Nomura, Ichiro Kawashima, Seiji Uenohara, Yuichiro Tanaka, Akinobu Mizutani, Kensuke Takada, Katsumi Tateno, Hakaru Tamukoh,Takashi Morie

The 3rd International Symposium on Neuromorphic AI Hardware P-PM12 2022年03月

Demonstration of in-materio reservoir computing with single-walled carbon nanotube/porphyrin-polyxoxmetalate random network 査読有り

Deep Banerjee, Saman Azhari, Yuki Usami, Hakaru Tamukoh,Hirofumi Tanaka

The 3rd International Symposium on Neuromorphic AI Hardware P-AM2 2022年03月

Numerical Simulation for Analog VLSI Implementation of Reinforcement Learning Using Reservoir Computing 査読有り

Katsunori Tamai, Kohei Kawazoe, Yuka Shishido, Yuichi Katori, Hakaru Tamukoh, Osamu Nomura, and Takashi Morie

The 3rd International Symposium on Neuromorphic AI Hardware P-AM7 2022年03月

A Co-Design Environment for AI Hardware Simulation Using PyLTSpice 査読有り

Yuka Shishido, Kohei Kawazoe, Katsunori Tamai, Hakaru Tamukoh, Osamu Nomura, Takashi Morie

The 3rd International Symposium on Neuromorphic AI Hardware P-AM8 2022年03月

Memory Capacity of Reservoir Computing Using Chaotic Boltzmann Machines 査読有り国際誌

Kazuo Nakahara, Yuichi Katori, Hakaru Tamukoh, Osamu Nomura, Takashi Morie,

The 3rd International Symposium on Neuromorphic AI Hardware P-AM9 2022年03月

Image Enhancement for Improving The Accuracy of Visual SLAM in Backlit Scenes 査読有り

Yuga Yano, Yukiya Fukuda, Noriaki Suetake, Hakaru Tamukoh

The 3rd International Symposium on Neuromorphic AI Hardware P-AM11 2022年03月

A Study on Multimodal Learning for Object Recognition in Robots with Gate Structures 査読有り

Ikuya Matsumoto, Daiju Kanaoka,Hakaru Tamukoh

The 3rd International Symposium on Neuromorphic AI Hardware P-PM11 2022年03月

Implementation of ReLU Function in Stochastic Computing 査読有り国際誌

Kanta Yoshioka, Ichiro Kawashima and Hakaru Tamukoh

The 3rd International Symposium on Neuromorphic AI Hardware P-PM13 2022年03月

Backlit Scene Image Enhancement for Improving the Accuracy of Visual SLAM 査読有り国際誌

Yuga Yano, Yukiya Fukuda, Noriaki Suetake, Hakaru Tamukoh

27th International Symposium on Artificial Life and Robotics (AROB 27th 2022) 1003 - 1006 2022年01月

Automatic Approximation of Primitive Shapes using Point Clouds 査読有り国際誌

Yuma Yoshimoto,Hakaru Tamukoh

The 2022 International Conference on Artificial Life and Robotics (ICAROB2022) OS15-5 2022年01月

INT8 Activation Ternary or Binary Weights Networks 査読有り国際誌

Ninnart Fuengfusin, Hakaru Tamukoh

The 2022 International Conference on Artificial Life and Robotics (ICAROB2022) OS15-1 2022年01月

Automatic Approximation of Primitive Shapes using Point Clouds 査読有り

Yoshimoto Yuma, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 27 616 - 618 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes a method to estimate appropriate primitive shapes by automatically using a point cloud of objects. The process is as follows. First is to estimate a rotation angle of the object and, then place a primitive shape (i.e., cylinder or sphere) in the center of the object. The primitive shape is either stretched or compressed to fit the object. The distance between all the point cloud of the object and the primitive shape are measured. We apply these methods with various primitive shapes and find the most suitable primitive shape. We show that objects like apples and chikuwa can be recognized with primitive objects, such as spheres and cylinders.

DOI： 10.5954/icarob.2022.os15-5

INT8 Activation Ternary or Binary Weights Networks 査読有り

Fuengfusin Ninnart, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 27 599 - 603 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

We propose binary or ternary weights with 8-bit integer activation convolutional neural networks. This model is designed as a middle ground between 8-bit integer and 1-bit or 2-bit quantized models. We discover that conventional 1-bit or 2-bit only-weight quantization methods (i.e., BinaryConnect and Ternary weights network) can be utilized jointly with 8-bit integer activation quantization without significant fractions. Based on these two methods, we evaluate our model with a VGG16-like model and CIFAR10 dataset. Our model provides competitive results to a conventional floating-point model.

DOI： 10.5954/icarob.2022.os15-1

Emergence of In-Materio Intelligence from an Incidental Structure of a Single-Walled Carbon Nanotube–Porphyrin Polyoxometalate Random Network 査読有り国際誌

Deep Banerjee,Takumi Kotooka,Saman Azhari,Yuki Usami,Takuji Ogawa,James K. Gimzewski,Hakaru Tamukoh,Hirofumi Tanaka

Advanced Intelligent Systems 4 ( 4 ) 2022年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

Hardware-based machine intelligence with the network architecture of reservoir computing (RC) is gaining interest because of its biological computational resemblance along with an easy and efficient neural network training approach. Herein, such a physical RC (in-materio RC) platform consisting of a recurrent network formed by the single-walled carbon nanotube (SWNT)–porphyrin polyoxometalate (Por–POM) complex is demonstrated. The network architecture executes the fundamental reservoir properties of nonlinearity, higher harmonic generation, and 1/fγ power law information processing ability. Based on these functionalities, an RC benchmark task of waveform generation is performed where the device achieves maximum fitting accuracy of 99.4%. Furthermore, a supervised object classification task based on a one-hot vector target is also executed using Toyota Human Support Robot tactile inputs. The successful classification of objects of different hardness is enhanced when the device output response follows the 1/fγ power law of maximized information processing.

DOI： 10.1002/aisy.202100145

An Implementation Method Using Cut-Off Bits for Restricted Boltzmann Machines Without Random Number Generators 査読有り

Hori S., Tamukoh H.

IEEE Access 10 42791 - 42801 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This study proposes an implementation method of a hardware-oriented restricted Boltzmann machine (RBM) without random number generators (RNGs) that employ cut-off bits, which are obtained from fixed-point binary arithmetic operations on digital hardware, such as field-programmable gate arrays (FPGAs), instead of random numbers. Most FPGA circuits employ fixed-point binary arithmetic operations to improve hardware resource efficiency. Therefore, the proposed method applies the unique feature of the operation, which is bit width extension and cut-off bits. Stochastic neural networks, including RBMs, employ sampling processes based on a probability distribution associated with the network, and the processes require many random numbers. However, implementing RNGs in hardware is costly because it requires considerable hardware resources. The proposed method can mitigate this requirement. To validate the proposed method, we implement an RBM with the proposed method on the software, emulate fixed-point binary arithmetic operations, and train the RBM using the MNIST and Fashion MNIST datasets. Furthermore, we apply the chi-square goodness-of-fit test to evaluate the uniformity of the cut-off bits. Additionally, we compare hardware resource requirements and power consumption for the proposed method and some major RNGs, a linear feedback shift register (LFSR), and a xorshift. Experimental results showed that it was possible to use the cut-off bits for training the RBM using the datasets and clarified the properties of the cut-off bits using statistical analyses. Moreover, hardware implementation of the proposed method involved the lowest hardware resource requirements and power consumption among the RNGs compared in this study.

DOI： 10.1109/ACCESS.2022.3168026

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85129145824&origin=inward

Solution of World Robot Challenge 2020 Partner Robot Challenge (Real Space) 査読有り

Ono T., Kanaoka D., Shiba T., Tokuno S., Yano Y., Mizutani A., Matsumoto I., Amano H., Tamukoh H.

Advanced Robotics 36 ( 17-18 ) 870 - 889 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

The World Robot Challenge is an international competition for the social implementation of robots. Among them, the Partner Robot Challenge (Real Space) is a category that focuses on domestic service robots, competing to achieve simple tasks such as tidying a room, avoiding small obstacles, grasping a specified object from the shelf, and delivering an object to a waving person. In this category, we focused on the theme of ‘Keep Moving’ and worked on researching and developing technologies for object recognition, grasping, and other tasks. For object recognition, we propose an automatic dataset generator using a physics simulator and generate about 490,000 images in two hours. We trained it with an instance segmentation model, achieving an accuracy of about 79.3% in all games. For object grasping, we installed a three-dimensional sensor on the robot hand, and achieved successful grasping with high accuracy of about 79.6% (88.9% excluding failures due to hardware problems) in all games. In addition, we propose a motion synthesis method that simultaneously performs movement and posture transition to achieve high-speed motion. When navigation is executed, the robot changes to the navigation posture while aiming for the goal. Before reaching the goal, it changes the posture for the following action. As a result, we realized a speedup of about 1.32 times in the pick-and-place task. In the tidy-up task, the robot could clear an average of 14 objects and a maximum of 16 objects in 15 minutes. In the obstacle avoidance task, the robot succeeded five times out of six games, and in the delivery task, it succeeded four times out of six games. Throughout all the tasks, we scored 800 points twice, the highest score in the world, and won the championship. This research is aimed at the practical application of domestic service robots. We believe that the results presented in this paper demonstrate the practicality of domestic service robots in a well-developed environment. The video of the finals is available at https://youtu.be/ElUb8bfSC34?t=5511 (right side). The our source code using the simulator is available at https://github.com/Hibikino-Musashi-Home/hma_wrs_sim_ws.

DOI： 10.1080/01691864.2022.2115315

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85137831520&origin=inward

Reservoir-based convolution 査読有り

Tanaka Yuichiro, Tamukoh Hakaru

Nonlinear Theory and Its Applications, IEICE ( 一般社団法人電子情報通信学会 ) 13 ( 2 ) 397 - 402 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Reservoir computing (RC) has attracted attention and has been used in many applications because of its low training cost. Multiple studies using RC for image recognition have been proposed, and some have achieved accuracy rates of greater than 99% on the MNIST dataset. For the Fashion-MNIST and CIFAR-10 datasets, however, they have not yet achieved high accuracy. This study proposes a novel convolutional neural network based on RC that can be optimized by ridge regression rather than back-propagation. The reservoir-based network has multiple reservoirs with various leak rates to extract features with various spatial frequencies from the inputs. The experimental results show that the performance of the proposed model achieves higher accuracy rates in the mentioned datasets compared with those of other reservoir-based image recognition approaches.

DOI： 10.1587/nolta.13.397

その他リンク： https://kyutech.repo.nii.ac.jp/records/7914

Desgin and Implementation of ROS2-based Autonomous Tiny Robot Car with Integration of Multiple ROS2 FPGA Nodes 査読有り

Mori H., Amano H., Mizutani A., Okazaki E., Konno Y., Sada K., Ono T., Yoshimoto Y., Tamukoh H., Ohkawa T., Sugaya M.

FPT 2022 - 21st International Conference on Field-Programmable Technology, Proceedings 2022年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This paper introduces an autonomous tiny robot car equipped with a camera-based lane detection function and a traffic signal/obstacle, pedestrian recognition function. Each function is integrated by Robot Operating System 2 (ROS2), a middleware for robot system development. Autonomous driving without the need for a driver requires not only lane-following driving but also traffic signal recognition and obstacle recognition. These functions are implemented on FPGA, and we evaluated them. According to these results, the execution time of traffic signal recognition by FPGA was 1.2 to 3.4 times faster than CPU execution. YOLOv4 is used for obstacle recognition, which improved mAP by 3.79 points compared to YOLO v3-Tiny.

DOI： 10.1109/ICFPT56656.2022.9974433

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85145559746&origin=inward

INT8 Activation Ternary or Binary Weights Networks: Unifying Between INT8 and Lower-bit Width Quantization 査読有り

Fuengfusin N., Tamukoh H.

Journal of Robotics, Networking and Artificial Life ( 株式会社 ALife Robotics ) 9 ( 2 ) 171 - 176 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes ternary or binary weights with 8-bit integer activation convolutional neural networks. Our proposed model serves as the middle ground between 8-bit integer and lower than 8-bit precision quantized models. Our empirical experiments established that the conventional 1-bit or 2-bit only-weight quantization methods (i.e., BinaryConnect and ternary weights network) can be used jointly with the 8-bit integer activation quantization. We evaluate our model with the VGG16-like model to operate with the CIFAR10 and CIFAR100 datasets. Our models show competitive results to the general 32-bit floating point model.

DOI： 10.57417/jrnal.9.2_171

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85176926162&origin=inward

INT8 Activation Ternary or Binary Weights Networks 査読有り

Fuengfusin N., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 599 - 603 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125143140&origin=inward

Automatic Approximation of Primitive Shapes using Point Clouds 査読有り

Yoshimoto Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 616 - 618 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125142021&origin=inward

A memory-based entorhinal-hippocampal model and its FPGA implementation by on-chip RAMs 査読有り

Kawashima I., Tateno K., Morie T., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2022-May 491 - 495 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Artificial general intelligence, which imitates the human brain, is aspired. Episodic memories are considered to be a key feature in building human brain functions. This paper proposes a memory-based entorhinal-hippocampal model that encodes spatial and non-spatial information, essential to realize episodic memories. The model works as a memory that stores the location of objects and events as neural activity packets. This paper also proposes an area-efficient hardware implementation method for field-programmable gate arrays (FPGAs). Our proposal utilizes on-chip random access memories (RAMs) to achieve a large-scale implementation of our model. Circuit simulations validated the behavior of our hardware-friendly model. The results of logic synthesis revealed the area efficiency of the FPGA implementation method that utilizes on-chip RAMs.

DOI： 10.1109/ISCAS48785.2022.9937768

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142534523&origin=inward

A Control and Data Transfer Platform for FPGA Applications 査読有り

Hori S., Tamukoh H.

Journal of Robotics, Networking and Artificial Life ( 株式会社 ALife Robotics ) 9 ( 1 ) 37 - 42 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Deep learning technology has made remarkable progress in recent years and has been adopted for a variety of applications such as smartphones and cloud servers. These systems employ dedicated processors to reduce power consumption and process massive amounts of data. In this paper, we propose a field-programmable gate array (FPGA) infrastructure for easy verification of user logic. The infrastructure makes it easy to communicate and control a host PC and user logic. We implemented two example logics, which were simple image processing and a restricted Boltzmann machine, into the infrastructure to confirm these features.

DOI： 10.57417/jrnal.9.1_37

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85178265499&origin=inward

Applying Center Loss to Multidimensional Feature Space in Deep Neural Networks for Open-set Recognition 査読有り

Kanaoka D., Tanaka Y., Tamukoh H.

Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 5 359 - 365 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

With the advent of deep learning, significant improvements in image recognition performance have been achieved. In image recognition, it is generally assumed that all the test data are composed of known classes. This approach is termed as closed-set recognition. In closed-set recognition, when an untrained, unknown class is input, it is recognized as one of the trained classes. The method whereby an unknown image is recognized as unknown when it is input is termed as open-set recognition. Although several open-set recognition methods have been proposed, none of these previous methods excel in terms of all three evaluation items: learning cost, recognition performance, and scalability from closed-set recognition models. To address this, we propose an open-set recognition method using the distance between features in the multidimensional feature space of neural networks. By applying center loss to the feature space, we aim to maintain the classification accuracy of closed-set recognition and improve the unknown detection performance. In our experiments, we achieved state-of-the-art performance on the MNIST, SVHN, and CIFAR-10 datasets. In addition, the proposed approach shows excellent performance in terms of the three evaluation items.

DOI： 10.5220/0010816600003124

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85143999490&origin=inward

Hardware-oriented Algorithm for Human Detection using GMM-MRCoHOG Features 査読有り

Takemoto R., Nagamine Y., Yoshihiro K., Shibata M., Yamada H., Tanaka Y., Enokida S., Tamukoh H.

Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 4 749 - 757 2022年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In this research, we focus on Gaussian mixture model-multiresolution co-occurrence histograms of oriented gradients (GMM-MRCoHOG) features using luminance gradients in images and propose a hardware-oriented algorithm of GMM-MRCoHOG to implement it on a field programmable gate array (FPGA). The proposed method simplifies the calculation of luminance gradients, which is a high-cost operation in the conventional algorithm, by using lookup tables to reduce the circuit size. We also designed a human-detection digital architecture of the proposed algorithm for FPGA implementation using high-level synthesis. The verification results showed that the processing speed of the proposed architecture was approximately 123 times faster than that of the FPGA implementation of VGG-16.

DOI： 10.5220/0010848100003124

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85182929508&origin=inward

A dataset generation for object recognition and a tool for generating ROS2 FPGA node 査読有り国際誌

Hayato Amano, Hayato Mori, Akinobu Mizutani, Tomohiro Ono, Yuma Yoshimoto, Takeshi Ohkawa, Hakaru Tamukoh

2021 International Conference on Field-Programmable Technology 2021年12月

An area-efficient multiply-accumulation architecture and implementations for time-domain neural processing 査読有り国際誌

Ichiro Kawashima, Yuichi Katori, Takashi Morie, Hakaru Tamukoh

2021 International Conference on Field-Programmable Technology 2021年12月

Reservoir-based Convolutional Neural Network 査読有り国際誌

Yuichiro Tanaka, Hakaru Tamukoh

The 2021 NonLinear Science Workshop 2021年12月

Cycle-Generative Adversarial Network for Generating a Pseudo Realistic Food Dataset Using RGB and Depth Images 査読有り

Obada Al aama, Yuma Yoshimoto and Hakaru Tamukoh

Journal of Advances in Artificial Life Robotics(JAALR) 2 ( 3 ) 345 - 350 2021年12月

Brain-inspired neural network navigation system with hippocampus, prefrontal cortex, and amygdala functions, 査読有り国際誌

Akinobu Mizutani, Yuichiro Tanaka, Hakaru Tamukoh, Yuichi Katori, Katsumi Tateno, Takashi Morie

2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) ( IEEE ) 2021年11月

Open Set Recognition Using the Feature Space of Deep Neural Networks 査読有り国際誌

Daiju Kanaoka, Yuichiro Tanaka, Hakaru Tamukoh

2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) ( IEEE ) 2021年11月

A Color Image Quantization Method Considering Chromatic Visual Impression 査読有り国際誌

Fukuda Y., Kubota R., Tamukoh H.

Proceedings of ISCIT 2021: 2021 20th International Symposium on Communications and Information Technologies: Quest for Quality of Life and Smart City 24 - 29 2021年10月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In recent years, many high-precision color image quantization (CIQ) methods have been proposed. However, they do not consider human visual impression, exposing the image to the possibility of altering its visual impression. In this paper, we propose a novel CIQ method that considers human visual impression. An impression is calculated by using a saliency map of an original image and the frequency of each of its colors to prevents CIQ from changing the impression of the image. Quantitative and qualitative evaluations of the experimental results demonstrate that the proposed method preserves the impressionistic colors better than the general CIQ methods.

DOI： 10.1109/ISCIT52804.2021.9590610

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85119601741&origin=inward

ReLU Functions using Finite State Machines for Stochastic Computing 査読有り国際誌

Kanta Yoshioka, Ichiro Kawashima, Hakaru Tamukoh

International Workshop on Smart Info-Media Systems in Asia RS1-5 40 2021年09月

記述言語：英語掲載種別：研究論文（学術雑誌）

This study presents novel ReLU functions-based on stochastic computing (SC-based ReLU). The conventional SC-based ReLU for an addition method using approximate parallel counter (APC), which outputs the sum of multiple inputs every clock cycle works as a ReLU using accumulator. However, this conventional method loses the high fault tolerance, which is one of the advantages of stochastic computing (SC). Therefore, we propose an SC-based ReLU using a finite state machine (FSM) instead of an accumulator for an addition method using a multiplexer (MUX) which determines the outputs stochastically. For example, if an error occurs in the value of the accumulator in the conventional method, all subsequent operations will be wrong. However, in the case of the proposed SC-based ReLU using FSM, even if an error occurs in the current state of the FSM, it quickly returns the correct state of the FSM. Thus, the proposed SC-based ReLU using FSM for MUX is more fault tolerant than the conventional SC-based ReLU using the accumulator. However, the accuracy of the addition methods using MUX is poor because the output is determined stochastically, and a large length of bitstream (BSL) is required for accurate calculation. As the BSL becomes larger, the latency for the calculation increases, decreasing the performance. Therefore, we propose a SC-based ReLU using FSM for APC. The addition method using APC doesn’t require BSL as large as the addition method using MUX to obtain correct calculation results. Compared with the conventional SC-based ReLU using an accumulator for APC, the proposed SC-based ReLU using FSM for APC shows sufficient accuracy even with a small BSL. The proposed SC-based ReLU using FSM has also a high fault tolerance,

DOI： 10.34385/proc.66.RS1-5

In-materio reservoir computing in a sulfonated polyaniline network 査読有り国際誌

Yuki Usami, Bram van de Ven, Dilu G. Mathew, Hakaru Tamukoh　他

Advanced Materials ( Wiley-VCH ) 33 ( 48 ) 2021年09月

A sub-model detachable convolutional neural network 査読有り

Fuengfusin N., Tamukoh H.

Journal of Robotics, Networking and Artificial Life 8 ( 1 ) 52 - 55 2021年06月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

In this research, we propose a Convolutional Network with sub-Networks (CNSN), i.e., a Convolutional Neural Network (CNN) or base-model that can be divided into sub-models on demand. The CNN architecture, entails that feature map shapes are varied throughout the model, therefore, the hidden layer within CNN may not directly process an input image without modification. To address this problem, we propose a step-down convolutional layer, which is a convolutional layer acting as an input layer for the sub-model. This step-down convolutional layer reshapes and processes an input image to a preferred representation to the sub-model. To train CNSN, we treat the base-model and sub-models as distinct models. Each model is forward-and back-propagated separately. Using multi-model loss, i.e., a linear combination of losses from base-model and sub-models, we thus update model parameters that can be utilized in both base-model and sub-models.

DOI： 10.2991/jrnal.k.210521.012

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FPGA Implementation of Pulse-Coupled Phase Oscillators working as a Reservoir at the Edge of Chaos 査読有り国際誌

Dinda Pramanta, Hakaru Tamukoh

2021 IEEE International Symposium on Circuits and Systems 2021年05月

Mixed-precision weights network for field-programmable gate array 査読有り

Fuengfusin N., Tamukoh H.

PLoS ONE 16 ( 5 May 2021 ) 2021年05月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

In this study, we introduced a mixed-precision weights network (MPWN), which is a quantization neural network that jointly utilizes three different weight spaces: binary {−1,1}, ternary {−1,0,1}, and 32-bit floating-point. We further developed the MPWN from both software and hardware aspects. From the software aspect, we evaluated the MPWN on the Fashion-MNIST and CIFAR10 datasets. We systematized the accuracy sparsity bit score, which is a linear combination of accuracy, sparsity, and number of bits. This score allows Bayesian optimization to be used efficiently to search for MPWN weight space combinations. From the hardware aspect, we proposed XOR signed-bits to explore floating-point and binary weight spaces in the MPWN. XOR signed-bits is an efficient implementation equivalent to multiplication of floating-point and binary weight spaces. Using the concept from XOR signed bits, we also provide a ternary bitwise operation that is an efficient implementation equivalent to the multiplication of floating-point and ternary weight space. To demonstrate the compatibility of the MPWN with hardware implementation, we synthesized and implemented the MPWN in a field-programmable gate array using high-level synthesis. Our proposed MPWN implementation utilized up to 1.68-4.89 times less hardware resources depending on the type of resources than a conventional 32-bit floating-point model. In addition, our implementation reduced the latency up to 31.55 times compared to 32-bit floating-point model without optimizations.

DOI： 10.1371/journal.pone.0251329

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Object Recognition Using Flexible Tactile Sensor 査読有り国際誌

Shoshi Tokuno, Yuichiro Tanaka, Takumi Kawasetsu, Koh Hosoda, and Hakaru Tamukoh

Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform 2020 81 - 82 2021年03月

Network with sub-networks: Layer-wise detachable neural network 査読有り

Fuengfusin N., Tamukoh H.

Journal of Robotics, Networking and Artificial Life 7 ( 4 ) 240 - 244 2021年03月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

In this paper, we introduce a network with sub-networks: a neural network whose layers can be detached into sub-neural networks during the inference phase. To develop trainable parameters that can be inserted into both base-model and sub-models, first, the parameters of sub-models are duplicated in the base-model. Each model is separately forward-propagated, and all models are grouped into pairs. Gradients from selected pairs of networks are averaged and used to update both networks. With the Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets, our base-model achieves identical test-accuracy to that of regularly trained models. However, the sub-models result in lower test-accuracy. Nevertheless, the sub-models serve as alternative approaches with fewer parameters than those of regular models.

DOI： 10.2991/jrnal.k.201215.006

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An efficient hardware-oriented dropout algorithm 査読有り

Yeoh Y.J., Morie T., Tamukoh H.

Neurocomputing 427 191 - 200 2021年02月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes a hardware-oriented dropout algorithm, which is efficient for field programmable gate array (FPGA) implementation. In deep neural networks (DNNs), overfitting occurs when networks are overtrained and adapt too well to training data. Consequently, they fail in predicting unseen data used as test data. Dropout is a common technique that is often applied in DNNs to overcome this problem. In general, implementing such training algorithms of DNNs in embedded systems is difficult due to power and memory constraints. Training DNNs is power-, time-, and memory- intensive; however, embedded systems require low power consumption and real-time processing. An FPGA is suitable for embedded systems for its parallel processing characteristic and low operating power; however, due to its limited memory and different architecture, it is difficult to apply general neural network algorithms. Therefore, we propose a hardware-oriented dropout algorithm that can effectively utilize the characteristics of an FPGA with less memory required. Software program verification demonstrates that the performance of the proposed method is identical to that of conventional dropout, and hardware synthesis demonstrates that it results in significant resource reduction.

DOI： 10.1016/j.neucom.2020.11.055

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098454549&origin=inward

Hardware-oriented algorithm of reservoir computing based on chaotic Boltzmann machines 査読有り国際誌

Ichiro Kawashima, Yuichi Katori, Takashi Morie and Hakaru Tamukoh,

the volume of Journal of Physics: Conference Series (JPCS) 2021年02月

Synthesis of realistic food dataset using generative adversarial network based on RGB and depth images 査読有り

Al aama Obada, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 26 16 - 19 2021年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Constructing a large food dataset is time and effort consuming due to the need to cover the feature variations of food items. Hence, a huge data is needed for training neural networks. This paper aims to advocate the Cycle-GAN to build up large food dataset based on large number of simulated images and relatively few real captured images thus obtaining more realistic images effortlessly compared with traditional capturing. Real RGB and depth images of variant food samples allocated over turntable were captured in three different angles using real-sense depth camera with different backgrounds. Furthermore, for simulated images, the Autodesk 3D_Maya software was employed using the same parameters of captured real images. Results showed that generally, realistic style transfer on simulated food objects was obtained as a result of employing Cycle-GAN. GAN proved to be an efficient tool that could minimize imaging efforts resulting in realistic images.

DOI： 10.5954/icarob.2021.os19-4

Influence of fpga implementation methods in high-level synthesis 査読有り

Watanabe Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2021 10 - 11 2021年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

We explain about how the difference of implementation methods written in C++ in High-Level Synthesis (HLS) influences on latency for tiny You only look once (YOLO) v2, a real-time object detection system to infer on an FPGA. To utilize features of FPGA, we need to implement hardware-oriented algorithms such as the weight binalization. We primarily focus on convolution in tiny YOLO v2 network and we report execution results on the Xilinx SDSoC development environment to know whether methods are appropriate or not.

DOI： 10.5954/ICAROB.2021.OS19-2

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108833369&origin=inward

Convolutional network with sub-networks 査読有り

Fuengfusin N., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2021 6 - 9 2021年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

We propose a convolutional network with sub-networks (CNSN), a convolutional neural network (CNN) that can be detached into sub-models on fly. Due to a conventional design of CNN, shapes of feature map are varied throughout the model. Therefore, the hidden layer within CNN may not directly process the input image without any modifications. To address this problem, we propose a step-down convolutional layer, a convolutional layer which acts as an input layer for the sub-model. The step-down convolutional layer reshapes the input image to a preferred representation to the sub-model. To train CNSN, we treat the base-model and sub-models as different models. We separately forward-and back-propagate each model. By using multi-model loss, a linear combination of losses from base-and sub-models, we can update weights that can be utilized in both base-and sub-models.

DOI： 10.5954/ICAROB.2021.OS19-1

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108779535&origin=inward

A hardware-oriented random number generation method and a verification system for fpga 査読有り

Hori S., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2021 12 - 15 2021年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Deep learning technology has made remarkable progress in recent years and has been applied to a variety of applications such as smartphones and cloud servers. These systems employ dedicated processors to save power consumptions and process massive data. In this paper, we introduce a hardware-oriented restricted Boltzmann machine and propose a field-programmable gate array (FPGA) infrastructure for easy verification of user circuits. The infrastructure makes it easy to communicate and control between the host PC and the user circuit.

DOI： 10.5954/ICAROB.2021.OS19-3

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108826978&origin=inward

In-Materio Reservoir Computing in a Sulfonated Polyaniline Network 査読有り

Usami Y., van de Ven B., Mathew D.G., Chen T., Kotooka T., Kawashima Y., Tanaka Y., Otsuka Y., Ohoyama H., Tamukoh H., Tanaka H., van der Wiel W.G., Matsumoto T.

Advanced Materials 2021年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

A sulfonated polyaniline (SPAN) organic electrochemical network device (OEND) is fabricated using a simple drop-casting method on multiple Au electrodes for use in reservoir computing (RC). The SPAN network has humidity-dependent electrical properties. Under high humidity, the SPAN OEND exhibits mainly ionic conduction, including charging of an electric double layer and ionic diffusion. The nonlinearity and hysteresis of the current–voltage characteristics progressively increase with increasing humidity. The rich dynamic output behavior indicates wide variations for each electrode, which improves the RC performance because of the disordered network. For RC, waveform generation and short-term memory tasks are realized by a linear combination of outputs. The waveform task accuracy and memory capacity calculated from a short-term memory task reach 90% and 33.9, respectively. Improved spoken-digit classification is realized with 60% accuracy by only 12 outputs, demonstrating that the SPAN OEND can manage time series dynamic data operation in RC owing to a combination of rich dynamic and nonlinear electronic properties. The results suggest that SPAN-based electrochemical systems can be applied for material-based computing, by exploiting their intrinsic physicochemical behavior.

DOI： 10.1002/adma.202102688

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85114995164&origin=inward

Synthesis of realistic food dataset using generative adversarial network based on rgb and depth images 査読有り

Al Aama O., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2021 16 - 19 2021年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108817301&origin=inward

Fpga implementation of a binarized dual stream convolutional neural network for service robots 査読有り

Yoshimoto Y., Tamukoh H.

Journal of Robotics and Mechatronics ( 富士技術出版株式会社 ) 33 ( 2 ) 386 - 399 2021年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

In this study, with the aim of installing an object recognition algorithm on the hardware device of a service robot, we propose a Binarized Dual Stream VGG-16 (BDS-VGG16) network model to realize high-speed computations and low power consumption. The BDS-VGG16 model has improved in terms of the object recognition accuracy by using not only RGB images but also depth images. It achieved a 99.3% accuracy in tests using an RGB-D Object Dataset. We have also confirmed that the proposed model can be installed in a field-programmable gate array (FPGA). We have further installed BDS-VGG16 Tiny, a small BDS-VGG16 model in XCZU9EG, a system on a chip with a CPU and a middle-scale FPGA on a single chip that can be installed in robots. We have also integrated the BDS-VGG16 Tiny with a robot operating system. As a result, the BDS-VGG16 Tiny installed in the XCZU9EG FPGA realizes approximately 1.9-times more computations than the one installed in the graphics processing unit (GPU) with a power efficiency approximately 8-times higher than that installed in the GPU.

DOI： 10.20965/jrm.2021.p0386

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105921543&origin=inward

FPGA implementation of pulse-coupled phase oscillators working as a reservoir at the edge of chaos 査読有り

Pramanta D., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2021-May 2021年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In the field of neuroscience, reservoir computing (RC) has been viewed as a model of the neuron computational system. RC is a framework for constructing recurrent neural networks, which is used for modeling the parts of the brain to solve the temporal problem. We construct the network inside the reservoir using the Pulse-Coupled Phase Oscillator (PCPO) with neighbor topology connections on field-programmable-gate-array (FPGA). Winfree model is used for PCPO spiking-based. We investigate the stability of the edge phenomenon using the zero one test (Z1-Test) methodology. We evaluate the proposed model on the time series generation tasks. We have successfully implemented and verified using FPGA that the 3x3 and 10x10 PCPO working as a Reservoir on the network.

DOI： 10.1109/ISCAS51556.2021.9401215

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An area-efficient multiply-accumulation architecture and implementations for time-domain neural processing 査読有り

Kawashima I., Katori Y., Morie T., Tamukoh H.

2021 International Conference on Field-Programmable Technology, ICFPT 2021 2021年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In our work, a new area-efficient multiply-accumulation scheme for time-domain neural processing named differential multiply-accumulation is proposed. Our new scheme reduces hardware resources utilization of multiply-accumulation with suppressing the increasing computational time resulting from the time-multiplexing. As a result, 2,048 neurons of fully connected CBM and RC-CBM were synthesized for a single field-programmable gate array (FPGA).

DOI： 10.1109/ICFPT52863.2021.9609809

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Cycle-Generative Adversarial Network for Generating a Pseudo Realistic Food Dataset Using RGB and Depth Images 査読有り

aama Obada Al, Yoshimoto Yuma, Tamukoh Hakaru

Journal of Advances in Artificial Life Robotics ( 株式会社 ALife Robotics ) 2 ( 3 ) 128 - 133 2021年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Constructing a food dataset is time and effort consuming due to the requirement for covering the feature variations of food samples. Additionally, a large dataset is needed for training neural networks. Generative adversarial networks (GANs) are a recently developed technique to learn deep representations without extensively annotated training data. They can be used in several applications, including generating food datasets. This paper advocates the use of Cycle-GAN to generate a large pseudo-realistic food dataset based on a large number of simulated images and a small number of real images in comparison to traditional techniques. A single depth camera in three different angles and a turntable are arranged to capture real RGB-D images of food samples. 3D modeling software is used to generate simulated images using the same configuration of captured real images. Results showed that Cycle-GAN realistic style transfer on simulated food objects is achievable, and that it can be an efficient tool to minimize real image capturing efforts.

DOI： 10.57417/jaalr.2.3_128

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001340

深層ニューラルネットワークを用いた領域推定と部分テンプレートマッチングによる道路標示検出システム査読有り

三井悠也, 宮崎椋瑚, 吉元裕真, 石田裕太郎, 伊藤太久磨, 通山恭一, 田向権

知能と情報 ( 日本知能情報ファジィ学会 ) 33 ( 1 ) 566 - 571 2021年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

本研究では，自動運転技術における自車位置推定のために，You Only Look Once（YOLO）を処理に組み込むことで道路標示検出システムの性能向上を行う．従来手法では，輝度値に基づくテンプレートマッチングで道路標示を検出する．しかし，この手法では日光による白飛びや標示の掠れにより検出できないものが存在する．これに対し，提案手法ではYOLOで道路標示の存在する領域を探索し，テンプレートマッチングの適用範囲を制限する．これにより，誤検出の発生を抑制でき，テンプレートマッチングの検出閾値を下げ，これまで未検出であった道路標示の数を削減できる．また，テンプレートマッチングの探索領域も制限され，処理速度も向上できる．実験の結果，提案手法は誤検出数は0のまま，従来手法よりも未検出数を低減できた．また，従来手法と比較してシステムのAccuracyは0.013，処理速度は4.6 FPS上がった．

DOI： 10.3156/jsoft.33.1_566

その他リンク： https://ci.nii.ac.jp/naid/130007986500

A hardware-oriented algorithm of GMM-MRCoHOG for high-performance human detection by an FPGA 査読有り

Nagamine Y., Yoshihiro K., Shibata M., Yamada H., Enokida S., Tamukoh H.

Proceedings of SPIE - The International Society for Optical Engineering 11766 2021年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In this study, we propose a hardware-oriented Gaussian mixture model - multiresolution co-occurrence histograms of oriented gradients (GMM-MRCoHOG) algorithm for efficient human detection by a field-programmable gate array (FPGA). GMM-MRCoHOG is a HOG-based human detection method in which the computation of angles is quantized to 36 directions and 2D Gaussian distribution computation causes a decrease in processing speed and an increase in hardware resource usage. We propose a hardware-oriented algorithm to solve these problems. First, we propose a rough angle computation method of comparison with a tangent table. Second, we propose a bit-shifting-based Gaussian distribution computation method. Experimental results show that the proposed hardware-oriented algorithm does not significantly reduce the detection accuracy of GMM-MRCoHOG. High-level synthesis results of the FPGA implementation show that fast, low-resource processing is possible.

DOI： 10.1117/12.2591024

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103287974&origin=inward

An Energy-Efficient Time-Domain Analog CMOS BinaryConnect Neural Network Processor Based on a Pulse-Width Modulation Approach 査読有り

Yamaguchi M., Iwamoto G., Nishimura Y., Tamukoh H., Morie T.

IEEE Access 9 2644 - 2654 2021年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes a time-domain analog calculations model based on a pulse-width modulation (PWM) approach for neural network calculations including weighted-sum or multiply-and-accumulate calculation and rectified-linear unit operation. We also propose very-large-scale integration (VLSI) circuits to implement the proposed model. Unlike the conventional analog voltage or current mode circuits, our circuits use transient operation in charging/discharging processes to capacitors through resistors. Since the circuits calculate multiple weighted-sums by charging a capacitance, they can be operated with extremely low energy consumption. However, because a relatively long time constant is required to guarantee calculation resolution in the time domain, they have to use very high-resistance devices, on the order of giga-ohms. We designed, fabricated, and tested a proof-of-concept complementary metal-oxide-semiconductor (CMOS) VLSI chip using a 250-nm fabrication technology to verify weighted-sum operation based on the proposed model with binary weights and PWM input signals, which realizes the BinaryConnect model. In the chip, memory cells of static-random-access memory (SRAM) are used for synaptic connection weights. High-resistance operation was realized by using the subthreshold operation region of MOS transistors, unlike in the ordinary in-memory-computing circuits. We evaluated the energy efficiency and temperature characteristics by measurement using the fabricated chip, where the highest energy efficiency for the weighted-sum calculation was 300 TOPS/W (Tera-Operations Per Second per Watt). The effects by a temperature change can be compensated for by adjusting the bias voltage. If state-of-the-art VLSI technology is used to implement the proposed model, an energy efficiency of more than 1,000 TOPS/W will be possible.

DOI： 10.1109/ACCESS.2020.3047619

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099097466&origin=inward

A dataset generation for object recognition and a tool for generating ROS2 FPGA node 査読有り

Amano H., Mori H., Mizutani A., Ono T., Yoshimoto Y., Ohkawa T., Tamukoh H.

2021 International Conference on Field-Programmable Technology, ICFPT 2021 2021年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This paper introduces our autonomous driving system equipped with recognition processing units from a camera image for hazard object / human-doll detection and drive lane detection. In particular, this paper focuses on a dataset generation method for neural networks and a generation tool 'FPGA Oriented Easy Synthesizer Tool (FOrEST)' for ROS2-FPGA nodes. The results show that mAP of a neural network trained by the generated dataset is 94%, and a overhead of ROS2-FPGA communication by the FOrEST is 2-3 ms.

DOI： 10.1109/ICFPT52863.2021.9609880

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85123206616&origin=inward

Hardware-Oriented Pigeon-Inspired Optimization Algorithm and its FPGA Implementation 査読有り

Tatsuya Hayashi, Kentaro Honda, and Hakaru Tamukoh

Proc. of Int. Workshop on Smart Info-Media Systems in Asia (SISA2020) 71 - 76 2020年12月

Object Recognition Using Tactile Information and Its Application to Service Robots 査読有り

Daiju Kanaoka, Yutaro Ishida, and Hakaru Tamukoh

Proc. of Int. Workshop on Smart Info-Media Systems in Asia (SISA2020) 101 - 104 2020年12月

Multi-sampling Classifiers for the Cooking Activity Recognition Challenge 査読有り国際誌

Ninnart Fuengfusin, Hakaru Tamukoh

Human Activity Recognition Challenge. Smart Innovation, Systems and Technologies 199 2020年11月

Design and Implementation of Pulse-Coupled Phase Oscillators on a Field-Programmable Gate Array for Reservoir Computing 査読有り国際誌

Pramanta D., Tamukoh H.

Communications in Computer and Information Science 1333 333 - 341 2020年11月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2020, Springer Nature Switzerland AG. Reservoir computing (RC) has been viewed as a model of a neurological system. The RC framework constructs a recurrent neural network, which mimics parts of the brain, to solve temporal problems. To construct a neural network inside a reservoir, we adopt the pulse-coupled phase oscillator (PCPO) with neighbor topology connections on a field-programmable gate array (FPGA). Neural spikes for the PCPO are generated by the Winfree model. The low resource consumption of the proposed model in time-series generation tasks was confirmed in an evaluation study. We also demonstrate that on the FPGA, we can expand a 3 $$\times $$ 3 PCPO into a 10 $$\times $$ 10 PCPO, generate spike behavior, and predict the target signal with a maximum frequency of 418.796 MHz.

DOI： 10.1007/978-3-030-63823-8_39

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85097087623&origin=inward

FPGA Implementation and Verification of Reservoir Computing Based on Pseudo-Billiard Dynamics in Hypercube 査読有り国際誌

Daichi Yamamoto, Ichiro Kawashima, Hakaru Tamukoh, Takashi Morie, and Yuichi Katori

2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020) 264 - 264 2020年11月

Performance Evaluation of Reservoir Computing Using Pseudo-billiard Dynamics in Hypercube 査読有り国際誌

Daichi Kamimura, Yuichi Katori, Hakaru Tamukoh, and Takashi Morie

2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020) 135 - 135 2020年11月

A Reservoir Computing System Using a CMOS Chaotic Boltzmann Machine Chip Controlled by SoC FPGA 査読有り国際誌

Yuta Nishimura, Masatoshi Yamaguchi, Daichi Kamimura, Hakaru Tamukoh, and Takashi Morie

2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020) 137 - 137 2020年11月

A Brain-inspired Artificial Intelligence Model of Hippocampus, Amygdala, and Prefrontal Cortex on Home Service Robots 査読有り国際誌

Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Yuichi Katori, and Takashi Morie

2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020) 138 - 141 2020年11月

A Reservoir Based Q-learning Model for Autonomous Mobile Robots 査読有り国際誌

Masafumi Inada, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Takashi Morie, and Yuichi Katori

2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020) 213 - 216 2020年11月

Performance Evaluation of Reservoir Computing Using Pseudo-Billiard Dynamics in Hypercube 査読有り

Kamimura Daichi, Katori Yuichi, Tamukoh Hakaru, Morie Takashi

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 74 135 - 135 2020年11月

記述言語：英語掲載種別：研究論文（学術雑誌）

We proposed reservoir models using chaotic Boltzmann machines (CBMs) based on pseudo-billiard dynamics in hypercube. In order to utilize CBMs as a reservoir, we must control the chaotic behavior and satisfy the echo state property. We added an external reference clock signal to control the CBM. In this paper, we demonstrate the effect of an external reference clock signal and show that our model works as a reservoir by using NARMA task.

DOI： 10.34385/proc.74.a4l-c-1

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001359

FPGA Implementation and Verification of Reservoir Computing Based on Pseudo-Billiard Dynamics in Hypercube 査読有り

Yamamoto Daichi, Kawashima Ichiro, Tamukoh Hakaru, Morie Takashi, Katori Yuichi

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 74 264 - 264 2020年11月

記述言語：英語掲載種別：研究論文（学術雑誌）

Reservoir computing (RC) is a framework that constructs recurrent neural networks that can be utilized for generation, prediction, and discrimination of complex time-series. The recurrent connection on the RC is sparse and fixed, and thus, the computational cost of learning is small. The pseudo-billiard dynamics in a hypercube is known to be applicable for various types of computation. This hypercube based computing is very efficient for hardware implementation. In this research, we implement the hypercube based RC model in a digital circuit with hardware-oriented algorithms and verify its performance and effectiveness as a new artificial intelligence platform.

DOI： 10.34385/proc.74.b4l-b-4

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001342

A Brain-Inspired Artificial Intelligence Model of Hippocampus, Amygdala, and Prefrontal Cortex on Home Service Robots 査読有り

Tanaka Yuichiro, Tamukoh Hakaru, Tateno Katsumi, Katori Yuichi, Morie Takashi

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 74 138 - 141 2020年11月

記述言語：英語掲載種別：研究論文（学術雑誌）

We propose a brain-inspired artificial intelligence model that acquires local knowledge from a small number of human-robot interactions, and uses this knowledge for future prediction. We focus on areas of the brain: the hippocampus, the amygdala, and the prefrontal cortex, thereby providing an internal representation of place and time, a value judgment, and a prediction. By integrating these functions, our model achieves local knowledge acquisition and future prediction. The proposed model is evaluated in a task for home service robots. In the experiment, the model learns local time-series events from a small number of interactions, and predicts the future events.

DOI： 10.34385/proc.74.a4l-c-4

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001270

A Reservoir Computing System Using a CMOS Chaotic Boltzmann Machine Chip Controlled by SoC FPGA 査読有り

Nishimura Yuta, Yamaguchi Masatoshi, Kamimura Daichi, Tamukoh Hakaru, Morie Takashi

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 74 137 - 137 2020年11月

記述言語：英語掲載種別：研究論文（学術雑誌）

Reservoir computing (RC) has received increasing attention as a computational framework for recurrent neural networks. As an implementation of reservoir, using a chaotic Boltzmann machine (CBM) was proposed, and we prototyped a CMOS CBM LSI chip controlled by an external reference clock (CBM-crc LSI) for working as a reservoir. We have developed a system using a System-on-Chip Field programmable gate array (SoC FPGA). In this paper, we propose a reservoir system that consists of a CBM-crc LSI controlled by an SoC FPGA and show the results of the system operations.

DOI： 10.34385/proc.74.a4l-c-3

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001274

A Reservoir Based Q-Learning Model for Autonomous Mobile Robots 査読有り

Inada Masafumi, Tanaka Yuichiro, Tamukoh Hakaru, Tateno Katsumi, Morie Takashi, Katori Yuichi

IEICE Proceeding Series ( The Institute of Electronics, Information and Communication Engineers ) 74 213 - 216 2020年11月

記述言語：英語掲載種別：研究論文（学術雑誌）

We propose a robot navigation system using reinforcement learning and reservoir computing. Reservoir computing is a framework for the processing of complex time series based on a recurrent neural network (RNN). The proposed system is trained by reinforcement learning, which is a framework that performs trial and error based on the rewards given by the environment. The trained network generates motor commands reflecting on action values obtained by the reinforcement learning and operates the autonomous mobile robot.

DOI： 10.34385/proc.74.b3l-b-5

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001273

Live Demonstration: Hardware-Oriented Dual Stream Object Recognition System using Binarized Neural Networks 査読有り国際誌

Yuma Yoshimoto, Hakaru Tamukoh

IEEE International Symposium on Circuits and Systems (ISCAS) 2020年10月

ホームサービスロボットのための高精度な特定人物追跡システムの開発査読有り

小野智寛，阿部佑志，石田裕太郎，柴田智広，田向権

日本ロボット学会 38 2020年10月

A hardware intelligent processing accelerator for domestic service robots 査読有り

Ishida Y., Morie T., Tamukoh H.

Advanced Robotics 34 ( 14 ) 947 - 957 2020年07月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

We present a method for implementing hardware intelligent processing accelerator on domestic service robots. These domestic service robots support human life; therefore, they are required to recognize environments using intelligent processing. Moreover, the intelligent processing requires large computational resources. Therefore, standard personal computers (PCs) with robot middleware on the robots do not have enough resources for this intelligent processing. We propose a ‘connective object for middleware to an accelerator (COMTA),’ which is a system that integrates hardware intelligent processing accelerators and robot middleware. Herein, by constructing dedicated architecture digital circuits, field-programmable gate arrays (FPGAs) accelerate intelligent processing. In addition, the system can configure and access applications on hardware accelerators via a robot middleware space; consequently, robotic engineers do not require the knowledge of FPGAs. We conducted an experiment on the proposed system by utilizing a human-following application with image processing, which is commonly applied in the robots. Experimental results demonstrated that the proposed system can be automatically constructed from a single-configuration file on the robot middleware and can execute the application 5.2 times more efficiently than an ordinary PC.

DOI： 10.1080/01691864.2020.1769726

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85087072938&origin=inward

「自動運転のための知的処理」特集にあたって査読有り

我妻広明, 田向権, 大屋勝敬

知能と情報 ( 日本知能情報ファジィ学会 ) 32 ( 3 ) 54 - 55 2020年06月

Training autoencoder using three different reversed color models for anomaly detection 査読有り

Al aama O., Tamukoh H.

Journal of Robotics, Networking and Artificial Life 7 ( 1 ) 35 - 40 2020年06月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020 The Authors. Published by Atlantis Press SARL. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/). Autoencoders (AEs) have been applied in several applications such as anomaly detectors and object recognition systems. However, although the recent neural networks have relatively high accuracy but sometimes false detection may occur. This paper introduces AE as an anomaly detector. The proposed AE is trained using both normal and anomalous data based on convolutional neural network with three different color models Hue Saturation Value (HSV), Red Green Blue (RGB), and our own model (TUV). As a result, the trained AE reconstruct the normal images without change, whereas the anomalous image would be reconstructed reversely. The training and testing of the AE in case of RGB, HSV, and TUV color models were demonstrated and Cifar-10 dataset had been used for the evaluation process. It can be noticed that HSV color model has been more effective and achievable as an anomaly detector rather than other color models based on Z- and F-test analyses.

DOI： 10.2991/jrnal.k.200512.008

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85097084558&origin=inward

A hardware-oriented echo state network and its FPGA implementation 査読有り

Honda K., Tamukoh H.

Journal of Robotics, Networking and Artificial Life 7 ( 1 ) 58 - 62 2020年06月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020 The Authors. Published by Atlantis Press SARL. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/). This paper proposes implementation of an Echo State Network (ESN) to Field Programmable Gate Array (FPGA). The proposed method is able to reduce hardware resources by using fixed-point operation, quantization of weights, which includes accumulate operations and efficient dataflow modules. The performance of the designed circuit is verified via experiments including prediction of sine and cosine waves. Experimental result shows that the proposed circuit supports to 200 MHz of operation frequency and facilitates faster computing of the ESN algorithm compared with a central processing unit.

DOI： 10.2991/jrnal.k.200512.012

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85097052837&origin=inward

組込みソフトウェア開発とオントロジー査読有り

渡辺政彦, 市瀬龍太郎, 我妻広明, 田向権, 穴田啓樹

人工知能 ( 一般社団法人人工知能学会 ) 35 ( 2 ) 155 - 162 2020年03月

組込みソフトウェア開発とオントロジー査読有り

渡辺政彦，市瀬龍太郎，我妻広明，田向権，穴田啓樹

人工知能学会誌 35 ( 2 ) 155 - 162 2020年03月

Network with sub-networks 査読有り

Fuengfusin N., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2020 191 - 194 2020年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

We introduce network with sub-networks, a neural network which it’s weight layers can be detached into sub-neural networks during inference phase. To develop trainable parameters which can be inserted into both base-and sub-models, firstly, the parameters of sub-models are duplicated to base-model. Each model is forward-propagated separately. All models are grouped into pairs. Gradients from selected pairs of networks are averaged and updated both networks. With MNIST dataset, our base-model achieves the identical test-accuracy to the regularly trained models. In other hand, the sub-models are suffered an extend of loss in test-accuracy, nevertheless the sub-models provide alternative approaches to be deployed with less parameters compare to the regular model.

DOI： 10.5954/ICAROB.2020.OS20-2

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108824140&origin=inward

Anomaly detection using autoencoder trained with reversed color models 査読有り

Al Aama O., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2020 201 - 205 2020年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Autoencoders (AEs) have been applied in several applications such as anomaly detectors and object recognition systems. However, although the recent neural networks have relatively high accuracy but sometimes false detection may occur. This paper introduces AE as an anomaly detector. The proposed AE is trained using both normal and anomalous data based on convolutional neural network (CNN) with three different color models HSV, RGB and TUV (own model). As a result, the trained AE reconstruct the normal images without change, whereas the anomalous image would be reconstructed reversely. The training and testing of the autoencoder in case of RGB, HSV, and TUV color models were demonstrated and Cifar-10 dataset had been used for the evaluation process. It can be noticed that HSV color model has been more effective and achievable as an anomaly detector rather than other color models based on Z-test and F-test analyses.

DOI： 10.5954/ICAROB.2020.OS20-5

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108814747&origin=inward

A fast pick-and-place method for home service robots using 3d point clouds 査読有り

Ono T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 2020 195 - 196 2020年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Home service robots have begun attracting attention due to decreasing birthrate and increasing aging population. The basic functions of home service robots are object recognition, picking and placing, recognition of people and environment, and interaction with peoples. In this paper, we focus on object picking and placing in the domestic environment. The pick-and-place task is a very important technique used for arrangement shelves and tidying up rooms. In order for the robot to operate smoothly, the movement to pick-and-place the object must be fast. Therefore, we develop a fast pick-and-place method using 3D point clouds. Regarding the picking, we describe a grasping-point estimation method. Regarding the placing, we describe a placeable position estimation method. These methods are used at RoboCup@Home, an international competition aimed at the practical application of home service robots, and their effectiveness and validity are verified.

DOI： 10.5954/ICAROB.2020.OS20-3

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108862781&origin=inward

超立方体上の疑似ビリヤードダイナミクスに基づくレザバー計算のFPGA実装査読有り

山本大智, 川島一郎, 田向権, 森江隆

人工知能学会全国大会論文集 ( 一般社団法人人工知能学会 ) JSAI2020 ( 0 ) 3Rin422 - 3Rin422 2020年01月

記述言語：日本語掲載種別：研究論文（学術雑誌）

レザバー計算(RC)はリカレントニューラルネットワーク(RNN)の枠組みの1つで，時系列データの処理を目的に応用されている．機械学習モデルに対して，ディジタル回路実装を行うことで高速な演算速度を省電力で実現することが期待できる．香取らはディジタル回路との親和性が高いレザバー計算モデルを提案した．しかし，香取らのモデルをField Programmable Gate Array(FPGA)のようなディジタル回路に実装する場合，ニューロン数の変更に大きな制限が発生する．小規模な回路実装を行う手法として川嶋らがカオスボルツマンマシンのディジタル回路実装に用いたハードウェア指向アルゴリズムを提案している．本研究では川嶋らの提案したハードウェア指向アルゴリズムを基に対象のレザバー計算モデルを改良することで，ディジタル回路実装における回路リソースの消費を軽減可能なモデルを構築した．また，本研究のハードウェア指向アルゴリズムにより、レザバー計算の高速化の実現、人工知能構築のための新しいプラットフォームの確立が期待される．

DOI： 10.11517/pjsai.jsai2020.0_3rin422

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001366

Semi-automatic dataset generation for object detection and recognition and its evaluation on domestic service robots 査読有り

Ishida Y., Tamukoh H.

Journal of Robotics and Mechatronics ( 富士技術出版株式会社 ) 32 ( 1 ) 245 - 253 2020年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes a method for the semi-automatic generation of a dataset for deep neural networks to perform end-to-end object detection and classification from images, which is expected to be applied to domestic service robots. In the proposed method, the background image of the floor or furniture is first captured. Subsequently, objects are captured from various viewpoints. Then, the background image and the object images are composited by the system (software) to generate images of the virtual scenes expected to be encountered by the robot. At this point, the annotation files, which will be used as teaching signals by the deep neural network, are automatically generated, as the region and category of the object composited with the background image are known. This reduces the human workload for dataset generation. Experiment results showed that the proposed method reduced the time taken to generate a data unit from 167 s, when performed manually, to 0.58 s, i.e., by a factor of approximately 1/287. The dataset generated using the proposed method was used to train a deep neural network, which was then applied to a domestic service robot for evaluation. The robot was entered into the World Robot Challenge, in which, out of ten trials, it succeeded in touching the target object eight times and grasping it four times.

DOI： 10.20965/jrm.2020.p0245

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079900477&origin=inward

自動運転車における自車位置推定のためのランドマーク検出の改良査読有り

三井悠也, 福田有輝也, 宮﨑椋瑚, 石田祐太郎, 伊藤太久磨, 通山恭一, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 36 ( 0 ) 149 - 154 2020年01月

FPGA implementation of hardware-oriented chaotic Boltzmann machines 査読有り

Kawashima I., Morie T., Tamukoh H.

IEEE Access 8 204360 - 204377 2020年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Boltzmann machines (BMs) are useful in various applications but are limited by their requirement to generate random numbers. In contrast, chaotic Boltzmann machines (CBMs) are neural networks that imitate the stochastic behavior of BMs with the chaotic dynamics and deterministic behavior, without random numbers. CBMs can potentially require fewer hardware resources than the original algorithms due to the unnecessity of random number generators. In this study, hardware-oriented algorithms and a differential multiply-accumulation operation are proposed to overcome the difficulties of implementing CBMs on field-programmable gate arrays (FPGAs). A hardware-oriented algorithm for CBMs, which includes fixed-point operations and shift operations, is proposed to reduce hardware resource utilization in the implemented circuits. In particular, the differential multiply-accumulate operation allows us to implement the multiply-accumulate operation with block random access memory and digital signal processors to reduce the consumption of lookup tables and flip-flops in FPGAs without losing the calculation speed. Our proposed approach was evaluated in numerical simulations, logical synthesis, and FPGA implementation. The calculation speed of FPGA-implemented CBMs was compared with software-implemented CBMs, which resulted in 1 / 6,500 of calculation time reduction in a 300-neuron CBM. Moreover, 2,048 neurons of CBM were realized by the logical synthesis. Therefore, the proposed hardware implementation of CBMs was shown to be feasible. The proposed CBMs can solve combinatorial optimization problems at a larger scale with fewer resources.

DOI： 10.1109/ACCESS.2020.3036882

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102831540&origin=inward

Hardware-oriented dual stream object recognition system using binarized neural networks 査読有り

Yoshimoto Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2020-October 2020年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Service robots require an object recognition system to ensure their effective functioning in situations where Convolutional Neural Networks (CNN) are the mainstream machine learning technique employed by the system. Particularly, “Dual Stream VGG-16 (DS-VGG16),” which uses RGB and depth images, has been reported to have high accuracy for object recognition. However, it is difficult to implement CNN in robots, because it requires high computation power and consumes a huge amount of power. Although, implementing CNN with Field Programmable Gate Array (FPGA) solves the electric power problem, however it is difficult due to limited resources available. This paper proposes “Binarized Dual Stream VGG-16 (BDSVGG16),” which is Hardware-Oriented DS-VGG16. With the concept of Binarized Neural Networks (BNN), BDS-VGG16 is effective when implemented on FPGA. In results, the accuracy of BDS-VGG16 is 99.2%. It is higher than that of Eitel's model by 5.1 points. Further, we developed an object recognition system based on the Robot Operating System (ROS) which is a well-known middleware for robots, it uses our proposed method for service robot application.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105939592&origin=inward

An Amygdala-Inspired Classical Conditioning Model Implemented on an FPGA for Home Service Robots 査読有り

Tanaka Y., Morie T., Tamukoh H.

IEEE Access 8 212066 - 212078 2020年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This study develops an intelligent system for home service robots mimicking human brain function that can manage common knowledge applicable to any environment and local knowledge reflecting its specific environment. Deep learning is effective for acquiring common knowledge because the performance of deep learning relies on the amounts of training and big training data that can be accessed for such knowledge; however, deep learning is ineffective for acquiring local knowledge because no big training data for such knowledge exist. Thus, we propose a brain-inspired learning model and system for acquiring local knowledge using small training data. We focus on the amygdala because its classical fear conditioning is effective for training using small training data. We propose an amygdala-inspired classical conditioning model comprising multiple self-organizing maps (lateral nucleus) and a fully connected neural network (central nucleus), imitating the function and structure of the amygdala. The proposed model is applied to a task of a waiter robot in a restaurant, and the model can learn customers' preferences after only a few human-robot interactions. We accelerate the computation of the model and reduce its power consumption by proposing a hardware-oriented algorithm for the model and its digital hardware design and implement it in an XCZU9EG field programmable gate array. The hardware-oriented algorithm reduces the multiplication operations and exponential functions requiring huge hardware resources. The performance of the hardware operated at 150 MHz is 1,273 times faster than the software implementation on Arm Cortex-A53, and the power consumption of the chip is 5.009 W.

DOI： 10.1109/ACCESS.2020.3038161

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85097712435&origin=inward

Acceleration of training dataset generation by 3d scanning of objects 査読有り

Abe Y., Ishida Y., Ono T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics ( 株式会社ALife Robotics ) 2020 197 - 200 2020年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

A semi-automatic dataset generation system is effective to prepare a training dataset for object recognition in a personal residence. However, a semi-automatic that method requires significant manual processing to capture images of household objects. Therefore, we apply three-dimensional object scanning to eliminate manual processing and speedup dataset generation. Experimental results demonstrate that the proposed method can generate the dataset 40 minutes faster than a comparable previous method that did not require manual processing.

DOI： 10.5954/ICAROB.2020.OS20-4

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108817228&origin=inward

A hardware-oriented echo state network for fpga implementation 査読有り

Honda K., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics ( 株式会社ALife Robotics ) 2020 187 - 190 2020年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This paper proposes implementation of an echo state network (ESN) to field programmable gate array FPGA). The proposed method is able to reduce hardware resources by using fixed-point operation, quantization of weights, which includes accumulate operations and efficient dataflow modules. The performance of the designed circuit is verified via experiments including prediction of sine and cosine waves. Experimental result shows that the proposed circuit supports to 200[MHz] of operation frequency and facilitates faster computing of the ESN algorithm compared with a central processing unit.

DOI： 10.5954/ICAROB.2020.OS20-1

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108782809&origin=inward

Prediction of Sensory Information and Generation of Motor Commands for Autonomous Mobile Robots using Reservoir Computing 査読有り

Masafumi Inada, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno

2019 International Symposium on Nonlinear Theory and Its Applications (NOLTA2019) 2019年12月

High-Level Synthesis System to Integrate SoC and ROS 査読有り

Yutaro Ishida, Hakaru Tamukoh

Asia Pacific Conference on Robot IoT System Development and Platform 2019 (APRIS2019) 2019年11月

Construction and verification of person tracking system in autonomous robot for education 査読有り

Ryogo Miyazaki, Hakaru Tamukoh

Asia Pacific Conference on Robot IoT System Development and Platform 2019 (APRIS2019) 2019年11月

A Quick Data Generation Method for Training Object Detection Algorithms in Home Environments 査読有り

Yuma Yoshimoto, Muhammad Farhan Mustafa, Wan Zuha Wan Hasan, Hakaru Tamukoh

2019 International Workshop on Smart Info-Media Systems in Asia (SISA2019) 7 - 10 2019年09月

FPGA-enabled binarized convolutional neural networks toward real-time embedded object recognition system for service robots 査読有り

Yoshimoto Y., Shuto D., Tamukoh H.

2019 4th IEEE International Circuits and Systems Symposium, ICSyS 2019 2019年09月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In this presentation, we report the results of applying a binarized Convolutional Neural Network (CNN) and a Field Programmable Gate Array (FPGA) for image-based object recognition. While the demand rises for robots with robust object recognition implemented with Neural Networks, a tradeoff between data processing rate and power consumption persists. Some applications utilise Graphics Processing Units (GPU), which results in high power consumption, thus undesirable for embedded systems, while the others communicate with cloud computers to minimise computational resources at the clients' side, i.e. robots, raising another concern that the robots are unable to perform object recognition without the servers and network connections. To overcome these difficulties, we propose an embedded object recognition system implemented with a binarized CNN and an FPGA. FPGAs consist of a matrix of reconfigurable logic gates allowing parallel computing which befit most image processing algorithms such as the CNN. We train the binarized CNN on one of our datasets that contain images of several kinds of food and beverages. The results of the experiments show that the binarized CNN with an FPGA maintains high accuracy as well as real-time computation, suggesting that the proposed system is suitable for robots to perform their tasks in a real-world environment without needing to communicate with a server.

DOI： 10.1109/ICSyS47076.2019.8982469

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85084282280&origin=inward

Reservoir Computing Based on Dynamics of Pseudo-Billiard System in Hypercube 査読有り

Katori Y., Tamukoh H., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2019-July 2019年07月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Reservoir computing (RC) is a framework for constructing recurrent neural networks with simple training rule and sparsely and randomly connected nonlinear units. The network (called reservoir) generates complex motion that can be used for many tasks including time series generation and prediction. We construct a reservoir based on the dynamics of the pseudo-billiard system that produce complex motion in a high-dimensional hypercube. In particular, we use the chaotic Boltzmann machine (CBM) whose units exhibit chaotic behavior in the hypercube. The units interact with each other in a time-domain manner through its binary state, and thus an efficient hardware implementation of the system is expected. In order to utilize the CBM as the reservoir, it is necessary to control its chaotic behavior for ensuring the echo state property of RC and establish encoding and decoding for input and output signal. For this purpose, we introduce a reference clock and analyze effects and properties of the reference input. We evaluate the proposed model on the time series generation tasks and show that the model works properly on a broad range of parameter values. Our approach presents a novel mechanism for time-domain information processing and a fundamental technology for a brain like artificial intelligence system.

DOI： 10.1109/IJCNN.2019.8852329

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85073235480&origin=inward

A Chaotic Boltzmann Machine Working as a Reservoir and Its Analog VLSI Implementation 査読有り国際誌

Yamaguchi M., Katori Y., Kamimura D., Tamukoh H., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2019-July 2019年07月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Reservoir computing is attracting great interest because of its high computing ability especially for time-series prediction, despite its simple structure and learning scheme. This paper proposes a reservoir computing hardware model using a chaotic Boltzmann machine (CBM) as the reservoir, which can achieve complex motion in a dynamical system on a high-dimensional hypercube. The CBM uses analog nonlinear dynamics, unlike the stochastic operation of the original Boltzmann machine model. To utilize CBMs as a reservoir, chaotic operation must be suppressed, and the echo state property should be satisfied. We modify the CBM model for simpler analog complementary metal-oxide-semiconductor very-large-scale integration (CMOS VLSI) implementation, and propose its use as a reservoir by adding an external reference clock signal. We then verify its proper operation by numerical simulation. We also refine the CMOS VLSI circuit design based on the proposed modified CBM model to improve power consumption and calculation precision.

DOI： 10.1109/IJCNN.2019.8852325

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A Hardware Implementation of Craik-O’Brien Effect-Based Contrast Improvement for Dichromats 査読有り

Tomohiro Ono, Ryosuke Kubota, Noriaki Suetake, Hakaru Tamukoh

2019 Joint International Workshop on Advanced Image Technology and International Forum on Medical Imaging in Asia (IWAIT-IFMIA2019) 2019年01月

Live demonstration: Hardware implementation of brain-inspired amygdala model 査読有り

Tanaka Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 IEEE This live demonstration presents a brain-inspired amygdala model. Amygdala is an area of the brain that is associated with fear conditioning, which is a type of classical conditioning. The model can learn preferences through human-robot interactions by application of classical conditioning to the model. Additionally, to develop a high speed and low power system, we design a hardware of the amygdala model, and implemented the hardware into field programmable gate array.

DOI： 10.1109/ISCAS.2019.8702213

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066814574&origin=inward

Live demonstration: A VLSI implementation of time-domain analog weighted-sum calculation model for intelligent processing on robots 査読有り

Yamaguchi M., Iwamoto G., Abe Y., Tanaka Y., Ishida Y., Tamukoh H., Morie T.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 IEEE This live demonstration presents a VLSI chip based on “Time-domain Analog Computing with Transient states (TACT)” approach for intelligent processing on robots. This TACT chip, fabricated using 250-nm CMOS technology, implements a time-domain analog weighted-sum calculation model with very high energy efficiency. We integrate the TACT chip into a robot via Robot Operating System (ROS) interfaces. A human tracking robot demonstration is performed by the TACT chip with energy efficiency of 300 TOPS/W.

DOI： 10.1109/ISCAS.2019.8702222

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066803614&origin=inward

Hardware implementation of brain-inspired amygdala model 査読有り

Tanaka Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 IEEE Deep neural networks (DNNs) have achieved state-of-the-art results in several computing tasks. However, the performance of these DNNs is reliant on the availability of large amounts of training data, which is not always present. We approached this problem by developing a brain-inspired amygdala model to achieve computer learning based on limited training data. The amygdala is an area of the brain associated with classical fear conditioning. The proposed amygdala model is composed of a single layer of deep self-organizing map network (deep SOM network) and a fully-connected neural network (FCNN), which imitates the function and structure of an amygdala. We applied the proposed amygdala model to a robot waiter task in a restaurant. The experimental results show that the model learned a customer's preferences after only a few human robot interactions. To develop the digital hardware of the amygdala model, we designed hardware for the deep SOM network and the FCNN and implemented them in an XCZU9EG field programmable gate array (FPGA). Our FPGA implementation of a deep SOM network with 272 neurons and an FCNN with three output neurons outperformed a software implementation on an Intel Core i5-3470 CPU by over 600 times.

DOI： 10.1109/ISCAS.2019.8702430

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066804926&origin=inward

A hardware implementation of Craik-O'Brien effect-based contrast improvement for dichromats 査読有り

Ono T., Kubota R., Suetake N., Tamukoh H.

Proceedings of SPIE - The International Society for Optical Engineering 11050 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 SPIE. In this study, we designed a digital hardware circuit for a field-programmable gate array (FPGA) to provide an effective contrast improvement algorithm for dichromats. The proposed method employs the Craik-O 'Brien (C-O) effect. The C-O effect is an optical illusion effect in which subjective contrast is created from contour information. In the proposed method, the contrast modification is only conducted around the contours of objects to apply the C-O effect for dichromats. To extract the contour information of objects, a T-model filter which only requires a one-line buffer is introduced. The proposed method can realize the C-O effect without using dividers and multipliers. Therefore, it is relatively simple to implement in the FPGA. Through experiments with software and logic simulation, the effectiveness and validity of the proposed method were evaluated.

DOI： 10.1117/12.2521580

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063911033&origin=inward

A color quantization method preserving infrequent salient colors and its implementation on mobile devices 査読有り

Fukuda Y., Misawa H., Tamukoh H., Kubota R., Cha B., Aso T.

Proceedings of SPIE - The International Society for Optical Engineering 11049 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© COPYRIGHT SPIE. In this paper, we propose a new color quantization method that can preserve infrequent salient colors of an original image. The infrequent salient colors mean that they are not dominant globally, but are dominant locally and are important to keep the impression of the original image. In the proposed method, color quantization is realized by k-means clustering and an input dataset for the clustering are adaptively and repeatedly modified based on local quantization errors to preserve the infrequent salient colors. The proposed method is implemented as an Android application to verify the feasibility of the use of the proposed method on mobile devices.

DOI： 10.1117/12.2521607

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063916118&origin=inward

Modified hierarchical k-nearest neighbor method with application to land-cover classification 査読有り

Hayashi T., Tamukoh H., Kubota R.

Proceedings of SPIE - The International Society for Optical Engineering 11049 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© COPYRIGHT SPIE. In this paper, we propose a land-cover classification method based on a modified hierarchical k-nearest neighbor (MHkNN) algorithm to achieve a high classification accuracy. The proposed method introduces a reliability measure for each training sample, which is defined as confidence in the sample belonging to each of the considered classes. The method performs the majority voting considering not only the number of the training samples, but also their reliabilities. The classification performance of the proposed method is compared to that of the conventional land-cover classification methods. The effectiveness of the proposed method is verified by applying it to real remote sensing images.

DOI： 10.1117/12.2521356

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063862786&origin=inward

高位合成によるORB-SLAMのFPGA実装と評価査読有り

岩渕甲誠, 松本茂樹, 松尾幸治, 石田裕太郎, 廣瀬尚三, 長瀬雅之, 田向権

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2019 ( 0 ) 1A1-F06 2019年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

In recent years, automated driving and AGVs are actively researched and developed. The important technique is Simultaneous Localization and Mapping (SLAM) which creates maps and self-location estimation at simultaneously. There are several kinds of SLAM depending on the sensor and algorithm used, ORB-SLAM uses only monocular camera. Since it is cheaper than other sensors, it is easy to board it into AGV etc. However, the calculation amount is large. Although high performance PCs is required, it is not able to board it into AGV etc., so we focused on FPGA. The FPGA development environment is also actively developed, and the development with high-level synthesis has become realistic. Therefore, in this research, in order to improve FPGA development efficiency, we implemented ORB-SLAM from open source software to FPGA.

DOI： 10.1299/jsmermd.2019.1a1-f06

その他リンク： https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_1A1-F06/_pdf

High-Speed Synchronization of Pulse-Coupled Phase Oscillators on Multi-FPGA 査読有り

Pramanta D., Tamukoh H.

Communications in Computer and Information Science 1143 CCIS 318 - 329 2019年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This study proposes High-Speed Synchronization of Pulse-Coupled Phase Oscillators on multiple field-programmable-gate-array (FPGA). Winfree model is used for oscillators communication based and two FPGAs are connected by a gigabit transceiver (GTX). In order to verify the effect of communication delay and in-phase synchronization phenomenon between FPGAs, we implement various number of oscillators on the multi-FPGA platform. Four-oscillator network achieved first spike synchronization over two FPGAs within 12.47 (formula presented)s and datastream bitrate up tp 3.2 Gbps. We have successfully expanded the network from the previous study and verified that the 10 (formula presented) 10 pulse-coupled phase oscillators synchronized over two FPGAs via high-speed serial communication with a 0.1 (formula presented)s delay and the network reached a steady synchronization state after the spike-count reached 100 with a maximum frequency 298.014 MHz.

DOI： 10.1007/978-3-030-36802-9_34

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社会実装を目指したホームサービスロボットの研究開発査読有り

小野智寛, 堀三晟, 石田裕太郎, 田中悠一朗, 吉元裕真, 阿部佑志, 武藤冬樹, 椛島康平, 福宿将士, 坂田拓馬, 吉井拓巳, 上村大地, 金丸和樹, 中村健太郎, 西村雄太, 森江隆, 田向権

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2019 ( 0 ) 1P2-I02 2019年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

Home service robots have begun attracting attention due to decreasing birthrate and increasing aging population. We,“Hibikino-Musashi@Home”team, develop home service robots, and participate international competitions; RoboCup@Home and World Robot Challenge (WRC), which are established to realize that robots will work and live with people in domestic environments. In this paper, we introduce our robots and technologies, especially an object recognition and manipulation system. We use You Only Look Once (YOLO) for object recognition. To train YOLO, a big dataset which is difficult to prepare in a short term is required. For this, we develop an automatic annotation system for YOLO which can generate 1.7 million training data, and we can complete training of YOLO in a day. To grasp objects, estimation of object orientations is required. Our system can detect the orientations by processing point clouds of objects. By using the system, we obtained about 30% of scores in Tidy Up Task in the WRC.

DOI： 10.1299/jsmermd.2019.1p2-i02

その他リンク： https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_1P2-I02/_pdf

決定表に基づく自動運転用判断システムの構築と評価査読有り

宮﨑椋瑚, 三井悠也, 石田裕太郎, 渡辺政彦, 宇井健一, 横田剛典, 市瀬龍太朗, 我妻広明, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 35 ( 0 ) 722 - 727 2019年01月

ハードウェア指向 GMM-MRCoHOG 特徴量を用いた人物検出査読有り

長嶺佑哉, 吉弘憲大, 柴田雅聡, 山田英夫, 榎田修一, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 35 ( 0 ) 715 - 719 2019年01月

Alternative Dropout for Hardware Implementation in Recurrent Neural Networks, 査読有り

Yoeng Jye Yeoh, Hakaru Tamukoh

Proc. of Int. Workshop on Smart Info-Media Systems in Asia (SISA2018) 101 - 104 2018年12月

Object Recognition System using Deep Learning with Depth Images for Service Robots 査読有り

Yuma Yoshimto, Hakaru Tamukoh

The 2018 International Symposium on Intelligent Signal Processing and Communication Systems 2018年11月

Special section on smart multimedia & communication systems

Tamukoh H., Suetake N.

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences ( IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences ) E101A ( 11 ) 1735 - 1736 2018年11月

Reverse Reconstruction of Anomaly Input Using Autoencoders 査読有り

Suzuki A., Tamukoh H.

ISPACS 2018 - 2018 International Symposium on Intelligent Signal Processing and Communication Systems 431 - 435 2018年11月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Autoencoders (AEs) are used in several applications, including dimension reducers and anomaly detectors. This paper introduces AEs as anomaly detectors. The proposed AEs are trained using both normal and anomalous data in a novel training manner. As a result, the trained AEs reconstruct normal input regularly and anomalous input irregularly, respectively. Consequently, reconstruction errors by anomalous input become increase and it is easy to detect as anomalous input. We implement anomaly detectors with the proposed AEs, and applying it to the MNIST and CIFAR10 datasets.

DOI： 10.1109/ISPACS.2018.8923117

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077075169&origin=inward

Object Recognition System using Deep Learning with Depth Images for Service Robots 査読有り

Yoshimoto Y., Tamukoh H.

ISPACS 2018 - 2018 International Symposium on Intelligent Signal Processing and Communication Systems 436 - 441 2018年11月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In an aging society with fewer children, service robots are expected to play an increasingly important role in people's lives. To realize a future with service robots, a generic object recognition system is necessary to recognize a wide variety of objects with a high degree of accuracy. Therefore, this study employs deep convolutional neural networks for the generic object recognition system. To improve the accuracy of object recognition, both RGB images and depth images can be used effectively. In this paper, we propose a new architecture 'Dual Stream - VGG16 (DS-VGG16)' for a deep convolutional neural network to train both the RGB images and depth images, and we also present a new training method for the proposed architecture. The experimental results indicate that the proposed architecture and training method are effective. Finally, we develop an object recognition system based on the proposed method that has an interface of robot operating system for integrating the system into service robots.

DOI： 10.1109/ISPACS.2018.8923533

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077053491&origin=inward

論理知識型AI に基づく自動運転のための危険予測システムの構築と評価査読有り

橋本康平, 石田裕太郎, 市瀬龍太郎, 我妻広明, 田向権

システム制御情報学会論文誌 ( 一般社団法人システム制御情報学会 ) 31 ( 5 ) 191 - 201 2018年08月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

Danger forecast and its avoidance are highly important for the design of automated driving system and the function is expected to be solved by artificial intelligences (AIs). However the reasoning process is unclear in the conventional scheme such as machine learning and as deep neural network models. In this paper, we focus on the ability of the logical reasoning based on the Semantic Web techniques, which is called knowledge-based AI, and demonstrate successfully its implementation into a module of Robot Operating System (ROS) to control a real vehicle. The processing speed of proposed system is evaluated using the real vehicle in the situation of the pedestrian avoidance in the crossroad.

DOI： 10.5687/iscie.31.191

その他リンク： https://ci.nii.ac.jp/naid/130007433558

A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate 査読有り

Tanaka H., Akai-Kasaya M., Termehyousefi A., Hong L., Fu L., Tamukoh H., Tanaka D., Asai T., Ogawa T.

Nature Communications 9 ( 1 ) 2018年07月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2018 The Author(s). In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. However, the integration density of present neuromorphic devices is much less than that of human brains. In this report, we present molecular neuromorphic devices, composed of a dynamic and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). We show experimentally that the SWNT/POM network generates spontaneous spikes and noise. We propose electron-cascading models of the network consisting of heterogeneous molecular junctions that yields results in good agreement with the experimental results. Rudimentary learning ability of the network is illustrated by introducing reservoir computing, which utilises spiking dynamics and a certain degree of network complexity. These results indicate the possibility that complex functional networks can be constructed using molecular devices, and contribute to the development of neuromorphic devices.

DOI： 10.1038/s41467-018-04886-2

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85050013375&origin=inward

A Hardware Accelerated Robot Middleware Package for Intelligent Processing on Robots 査読有り

Yutaro Ishida, Takashi Morie, Hakaru Tamukoh

International Symposium on Circuits and Systems (ISCAS2018) 1 - 5 2018年05月

Live Demonstration: A Hardware Accelerated Robot Middleware Package for Intelligent Processing on Robots 査読有り

Yutaro Ishida, Takashi Morie, Hakaru Tamukoh

International Symposium on Circuits and Systems (ISCAS2018) 1 - 1 2018年05月

論理知識型AIに基づく自動運転のための危険予測システムの構築と評価査読有り

橋本康平，石田裕太郎，市瀬龍太郎，我妻広明，田向権

システム制御情報学会論文誌 31 ( 5 ) 191 - 201 2018年05月

A Hardware Accelerated Robot Middleware Package for Intelligent Processing on Robots 査読有り

Ishida Y., Morie T., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2018-May 2018年04月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2018 IEEE. Service robots require implementation of intelligent processing, e.g., image processing. However, the computational resources of standard PCs typically used in service robots are not sufficient for such processes. Furthermore, robot middleware is widely used in many robots because such systems facilitate integration and are suitable for rapid prototyping. We propose a 'connective object for middleware to accelerator (COMTA),' which is a processing system that uses hardware accelerators, i.e., field programmable gate arrays (FPGAs), and robot middleware. Users can access the FPGAs in the proposed system via middleware interfaces; thus, complex internal circuits are not required. For human tracking using image processing, the proposed system can automatically generate from a single configuration file. The proposed system performs 3.3 times more efficiently relative to computation than standard PCs in robots.

DOI： 10.1109/ISCAS.2018.8351722

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057089804&origin=inward

Live Demonstration: A Hardware Accelerated Robot Middleware Package for Intelligent Processing on Robots 査読有り

Ishida Y., Morie T., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2018-May 2018年04月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2018 IEEE. This live demonstration presents a 'connective object for middleware to accelerator (COMTA),' an intelligent processing system that uses hardware accelerators (i.e., field programmable gate arrays (FPGAs)) and robot middleware. The key idea of COMTA is to automatically generate the system via robot middleware interfaces. To realize the proposed system, we have developed a block of programs called an 'object' in a hardware/software complex system. We demonstrate an implementation of a human tracking image processing application on a vehicle robot accelerated by COMTA. The demonstration system achieved 3.3 times better power efficiency than a general PCs.

DOI： 10.1109/ISCAS.2018.8351837

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057134814&origin=inward

A pulse-width-modulation mode CMOS integrated circuit implementation of threshold-coupled map 査読有り

Seiji Uenohara, Takashi Morie, Hakaru Tamukoh, and Kazuyuki Aihara

Nonlinear Theory and Its Applications 9 ( 2 ) 268 - 280 2018年04月

A shared synapse architecture for efficient FPGA implementation of autoencoders 査読有り

Akihiro Suzuki, Takashi Morie and Hakaru Tamukoh

PLoS ONE 13 ( 3 ) 2018年03月

A shared synapse architecture for efficient FPGA implementation of autoencoders 査読有り

Suzuki A., Morie T., Tamukoh H.

PLoS ONE 13 ( 3 ) 2018年03月

記述言語：英語掲載種別：研究論文（学術雑誌）

Copyright: © 2018 Suzuki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weights are shared between the synapses of an input and a hidden layer, and between the synapses of a hidden and an output layer. This architecture utilizes less of the limited resources of an FPGA than an architecture which does not share the synapse weights, and reduces the amount of synapse modules used by half. For the proposed circuit to be implemented into various types of AEs, it utilizes three kinds of parameters; one to change the number of layers’ units, one to change the bit width of an internal value, and a learning rate. By altering a network configuration using these parameters, the proposed architecture can be used to construct a stacked AE. The proposed circuits are logically synthesized, and the number of their resources is determined. Our experimental results show that single and stacked AE circuits utilizing the proposed shared synapse architecture operate as regular AEs and as regular stacked AEs. The scalability of the proposed circuit and the relationship between the bit widths and the learning results are also determined. The clock cycles of the proposed circuits are formulated, and this formula is used to estimate the theoretical performance of the circuit when the circuit is used to construct arbitrary networks.

DOI： 10.1371/journal.pone.0194049

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044091289&origin=inward

Mixed precision weight networks: training neural networks with varied precision weights 査読有り

Fuengfusin N., Tamukoh H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11302 LNCS 614 - 623 2018年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2018, Springer Nature Switzerland AG. We propose Mixed Precision Weight Networks (MPWNs), neural networks that jointly utilize weights with varied precision in the layers. MPWNs constrain the weight layers to either 1-bit binary {-1,1}, 2-bit ternary {-1,0,1}, or the original 32-bit full precision weights. Each weight space contains unique properties for instance, high classification accuracy, small number of bit, and high sparsity. Hence, the properties of MPWNs can be adjusted by varying the combinations and orders of the weight layers. In this study, we identify three heuristic rules for effectively setting each of the weight layers. Therefore, MPWNs successfully utilize the robust properties from each weight space while avoiding their disadvantages. We evaluated MPWNs with MNIST, CIFAR-10, and CIFAR-100 training datasets. Our evaluation revealed that MPWNs models trained on CIFAR-10 and CIFAR-100 achieved the best overall properties comparing to conventional methods.

DOI： 10.1007/978-3-030-04179-3_54

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85059067983&origin=inward

自動運転のための運転行動意思決定システム開発に向けた統合シミュレータの構築査読有り

三好竜平, 宮崎椋瑚, 橋本康平, 石田祐太郎, 渡辺政彦, 宇井健一, 市瀬龍太郎, 我妻広明, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 34 ( 0 ) 450 - 455 2018年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

自動運転は，人工知能（AI）によってコンピュータ上で実行される．事故が発生した場合，自動運転システムは事故の原因を分析するために意思決定の根拠を示すべきである．本報告では，意思決定の根拠を示す知識ベースAIの統合シミュレータを提案する．シミュレータを構築するには，Autoware，Gazebo，知識ベースAIを組み合わせる． Autowareは自動運転用のオープンソースソフトウェアであり，Gazeboは任意の道路環境をシミュレートすることができる．提案シミュレータを用いて，自動運転のための知識ベースAIの実用性を何度も検証することができる．実験結果は，提案されたシミュレータが意思決定の根拠の提示に成功していることを示している．

DOI： 10.14864/fss.34.0_450

その他リンク： https://ci.nii.ac.jp/naid/130007554747

AP SoCによるFPGAとRTミドルウェアの連携査読有り

岩渕甲誠, 内田大貴, 石田裕太郎, 長瀬雅之, 田向権

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2018 ( 0 ) 2A1 - G10 2018年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

In recent years, robots has come to have useful, but high load functions, e.g. navigating automotive movement, image recognition by deep learning. Usually, these functions are executed by high-spec CPU or GPUs. However, in practical use these devices cannot be implemented into robot, because of the size or power consumption. So in this research we implemented image filter function on FPGA included in AP SoC as a sample of high load function. And we have also implemented RT-Middleware and some RT-Components as a sample robot system which includes a component handling FPGA on CPU included in AP SoC. We show the effectiveness of the collaboration with FPGA and RT-Middleware to develop robot system.

DOI： 10.1299/jsmermd.2018.2A1-G10

その他リンク： https://ci.nii.ac.jp/naid/130007551490

競技会活動を通じたホームサービスロボットの研究開発査読有り

吉元裕真, 岩元剛毅, 橋本康平, 森江隆, 田向権, 堀三晟, 石田裕太郎, 木山雄太, 黒田裕貴, 田中悠一朗, 久野昌隆, 藤田啓斗, 新谷嘉也

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2018 ( 0 ) 2P2 - E02 2018年01月

記述言語：日本語掲載種別：研究論文（学術雑誌）

In real world applications of home service robots, they are required to be able to perform housekeeping or other tasks. "RoboCup@Home" is a competition for benchmarking home service robots. It includes tests to service robots for real world implementation. These tests require the combination of intelligent functions such as object detection and voice recognition. We have developed a home service robot "Exi@" and evaluated its performance by participating in RoboCup@Home. This paper describes real world implementation of robots and RoboCup@Home. The functions embedded in Exi@ and a task "Restaurant" are also explained.

DOI： 10.1299/jsmermd.2018.2P2-E02

その他リンク： https://ci.nii.ac.jp/naid/130007552042

ホームサービスロボットにおける命令文中の単語意味ベクトルに注目した最適行動識別査読有り

石田裕太郎, 能勢啓輔, 安藤充宏, 田向権

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2018 ( 0 ) 2P1 - B18 2018年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

Service robots must assemble and execute its own action based on user's voice instructions. Additionally, in order to assist human daily life, the service robots execute not a single action like an industrial robot but plurality of actions must be combined according to the situation. As the number of actions the robots can perform increases, phrase of the voice instructions increases to an enormous pattern. Therefore, the robots must understand the instructions by natural language processing. In this paper, we focus on general purpose service robot (GPSR) task of RoboCup@Home, which is a competition for performance of the service robots, and identify behavior of the robots from natural language by using word vectorization and machine learning.

DOI： 10.1299/jsmermd.2018.2P1-B18

その他リンク： https://ci.nii.ac.jp/naid/130007551866

A pulse-width-modulation mode CMOS integrated circuit implementation of threshold-coupled map 査読有り

Uenohara Seiji, Morie Takashi, Tamukoh Hakaru, Aihara Kazuyuki

Nonlinear Theory and Its Applications, IEICE ( 一般社団法人電子情報通信学会 ) 9 ( 2 ) 268 - 280 2018年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

We propose a coupled-map-lattice complementary-metal-oxide-semiconductor very-large-scale-integration (CMOS VLSI) circuit based on the threshold-coupled map (TCM) that has been proposed previously as a unidirectional connected network model exhibiting different spatiotemporal patterns according to its underlying nonlinear map and update scheme. We introduce mutual connections and arbitrarily valued connection weights into the TCM to realize cellular automata. In this study, we design, fabricate, and evaluate a CMOS integrated circuit with which to implement this extended TCM (ETCM). The ETCM is a universal Turing machine as confirmed in circuit experiments using the fabricated circuit, which can achieve Rule110 of a one-dimensional cellular automaton.

DOI： 10.1587/nolta.9.268

その他リンク： https://ci.nii.ac.jp/naid/130006602774

Robust Boat Detection System using HSV Adaptive Background Modeling and Multiresolution Co-occurrence Histograms of Oriented Gradients, 査読有り

Daisuke Shuto, Kouji Okumura, Naoki Matsubara, Shaoning Pang, Hakaru Tamukoh and Abdolhossein Sarrafzadeh

Proc. of Int. Workshop on Smart Info-Media Systems in Asia (SISA2017) 128 - 132 2017年09月

カーロボ連携大学院での自動車・ロボットの知能化高度化に向けた専門人材育成査読有り

森江隆, 石井和男, 我妻広明, 田向権, 榎田修一, 齊藤剛史, 松永良一, 松波勲, 大貝晴俊,

工学教育 65 ( 4 ) 51 - 56 2017年08月

A CMOS chaotic Boltzmann machine circuit and three-neuron network operation 査読有り

Yamaguchi M., Tamukoh H., Suzuki H., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2017-May 1218 - 1224 2017年06月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2017 IEEE. This paper proposes CMOS VLSI implementation of a chaotic Boltzmann machine (CBM) model, which uses analog nonlinear dynamics instead of stochastic operation as in the original Boltzmann machine model. The CBM model is suitable for efficient VLSI implementation of Boltzmann machines because it requires no random number generator circuits, which consume a considerable footprint on a VLSI chip as well as considerable power. We describe the design results of CMOS circuits of neuron and synapse units. The neuron circuit uses subthreshold operation of MOSFETs to realize the exponential function used in the CBM model. We also provide measurement results of a fabricated CMOS chip for single-neuron unit circuit operation and demonstrate chaotic behavior in a three-neuron network.

DOI： 10.1109/IJCNN.2017.7965991

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85031020313&origin=inward

Synchronization of Pulse-Coupled Phase Oscillators over Multi-FPGA Communication Links 査読有り

Dinda Pramanta, Takashi Morie, Hakaru Tamukoh

Journal of Robotics, Networking and Artificial Life 4 ( 1 ) 91 - 96 2017年06月

Implementation of multi-FPGA Communication using Pulse-Coupled Phase Oscillators 査読有り

Dinda Pramanta, Takashi Morie, Hakaru Tamukoh

Proc. of the 2017 International Conference On Artificial Life And Robotics 17 2017年01月

Multi-Valued Quantization Neural Networks toward Hardware Implementation 査読有り

Yoshiya Aratani, Yeoh Yoeng Jye, Akihiro Suzuki, Daisuke Shuto, Takashi Morie, Hakaru Tamukoh

Proc. of the 2017 International Conference On Artificial Life And Robotics 58 2017年01月

Implementation of Multi-FPGA Communication using Pulse-Coupled Phase Oscillators 査読有り

Pramanta Dinda, Morie Takashi, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics ) 22 128 - 131 2017年01月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes an implementation of multi-Field Programmable Gate Array (FPGA) communication using pulse-coupled phase oscillators. At first, we construct a digital phase oscillator circuit with adjustable in-phase parameters. While performing the synchronization task, the oscillators are connected into the first input and first output (FIFO) interface. The communication in-between of FPGAs will occur by enabling the following inputs and outputs on the FIFO through the Gigabit Transceiver (GTX) clock domain. Pulse-coupled phase oscillators of Winfree's model are utilized as a spike generator and we expect the ideal of model circuit will synchronize. We employ two FPGA boards of Virtex6 ML605 and implement four oscillators on a hardware level. Experimental results show that first spike synchronizing over two FPGAs takes 12.47 μsec with data bit speed stream 3.2Gbps.

DOI： 10.5954/icarob.2017.os9-1

Hardware implementation of deep self-organizing map networks 査読有り

Tanaka Y., Tamukoh H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10613 LNCS 439 - 441 2017年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing AG 2017. We aim to develop a recognition system of high accuracy and low power consumption by designing a digital circuit for deep neural networks (DNNs), and by implementing the circuit on field programmable gate arrays (FPGAs). DNNs include numerous multiply operations, whereas FPGAs include a limited number of multipliers. We aim to reduce the number of multiply operations generated by the algorithms within DNNs. Deep self-organizing map networks (DSNs) [1] are DNNs comprising self-organizing maps (SOMs) [2] as shown in Fig. 1. A hardware-oriented algorithm for SOMs has been proposed herein [3]. The algorithm represents SOMs by replacing multiply operations with bitshift operations. DSNs that include only a few multiply operations can then be represented by employing the algorithm. In this paper, we propose a hardware-oriented algorithm and a hardware architecture for DSNs. The hardware-oriented algorithm reduces multiply operations and exponential functions in a computation of SOM Module as shown in Fig. 1. In addition, we confirm that the algorithm does not worsen performance of DSN by a software simulation. Figure 2 shows error rates of DSN during learning MNIST Dataset [4]. The performance of the proposed algorithm achieve comparable results to the conventional algorithm. We also describe a DSN comprising three layers by Verilog-HDL as shown in Fig. 1, and implement it on a Xilinx Vertex-6 XC6VLX240T FPGA. Experimental results showed that the proposed DSN circuit estimates a label of an input image in 2 µs while the software implemented using an Intel Core i5-3470 (3.20 GHz) CPU estimates it in about 1 ms. Thus the hardware is 500 times faster than the software. Its logic utilization is shown in Table 1.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85034254406&origin=inward

Evaluation of hardware oriented MRCoHOG using logic simulation 査読有り

Yamasaki Y., Ooe S., Suzuki A., Kuno K., Yamada H., Enokida S., Tamukoh H.

VISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 5 341 - 345 2017年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Human detection require high speed and high accuracy processing. One of the high performance techniques of the detection is multi-resolution co-occurrence histogram of oriented gradients (MRCoHOG). Since the calculation of co-occurrence requires a huge amount of processing resources, it is difficult to realize real-time human detection with MRCoHOG. Accordingly, hardware implementation is considered to be effective. In this paper, a hardware oriented MRCoHOG is proposed. In the proposed method, we simplify complicated calculation such as multiplications and square root operation for efficient hardware implementation. Experimental results show that the proposed method achieves better human detection rate than the ordinary method. Moreover, MRCoHOG is implemented in a digital circuit with the proposed method. According to logic simulation of the proposed circuit, the processing speed of the hardware implementation is 466 times higher than the software implementation.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047884344&origin=inward

An implementation of a spiking neural network using digital spiking silicon neuron model on a SIMD processor 査読有り

Hori S., Zapata M., Madrenas J., Morie T., Tamukoh H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10613 LNCS 437 - 438 2017年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing AG 2017. We implement a digital spiking silicon neuron (DSSN) [1] in a single instruction multiple data (SIMD) processor. The SIMD processor is a scalable, reconfigurable, and real-time spiking neural network emulator based on field programmable gate arrays [2]. We implement the DSSN model in the SIMD processor for the first time. The behavior of the membrane potential of one neuron based on the DSSN model is shown in Fig. 1. The operation results of the SIMD processor with 16-bit fixed-point operation are compared with software simulation results based on 64-bit floating-point operation. From the results, it is concluded that the SIMD processor successfully emulated the behavior of the membrane potential. In addition, a full-connection network consisting of 100 neurons is simulated in a software using fixed-point binary numbers to evaluate the bit width for the SIMD processor. In this experiment, the network stores two patterns selected from [1]. In the recall phase, the first pattern with noise is given to this network to recall the pattern. Experimental results show that the network with 16-bit fixed-point numbers, each of which includes a 12-bit fraction, a 3-bit integer, and a 1-bit sign, successfully recalled the input pattern as shown in Fig. 2. Here, Muis a recall rate [1]. From this result, a large DSSN network simulation on the SIMD processor is promising.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85034266960&origin=inward

A hardware-oriented dropout algorithm for efficient FPGA implementation 査読有り

Yeoh Y., Morie T., Tamukoh H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10639 LNCS 821 - 829 2017年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing AG 2017. This paper proposes a hardware oriented dropout algorithm for an efficient field-programmable gate array (FPGA) implementation. Dropout is a regularization technique, which is commonly used in neural networks such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs), among others. To generate a dropout mask to randomly drop neurons during training phase, random number generators (RNGs) are usually used in software implementations. However, RNGs consume considerable FPGA resources in hardware implementations. The proposed method is able to minimize the resources required for FPGA implementation of dropout by performing a simple rotation operation to a predefined dropout mask. We apply the proposed method to MLPs and CNNs and evaluate them on MNIST and CIFAR-10 classification. In addition, we employ the proposed method in GoogLeNet training using own dataset to develop a vision system for home service robots. The experimental results demonstrate that the proposed method achieves the same regularized effect as the ordinary dropout algorithm. Logic synthesis results show that the proposed method significantly reduces the consumption of FPGA resources in comparison to the ordinary RNG-based approaches.

DOI： 10.1007/978-3-319-70136-3_87

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85035101597&origin=inward

カーロボ連携大学院での自動車・ロボットの知能化高度化に向けた専門人材育成査読有り

森江隆, 石井和男, 我妻広明, 田向権, 榎田修一, 齊藤剛史, 松永良一, 松波勲, 大貝晴俊

工学教育 ( 公益社団法人日本工学教育協会 ) 65 ( 4 ) 4_51 - 4_56 2017年01月

記述言語：日本語掲載種別：研究論文（学術雑誌）

The Joint Graduate School Intelligent Car and Robotics Course, called “Car-Robo Joint Graduate School” is an educational program that started in 2012 mainly at The Kitakyushu Science and Research Park (KSRP). Graduate schools of engineering in Kyushu Institute of Technology, the University of Kitakyushu, and Waseda University at KSRP participate in this program. The purpose of this program is to foster highly skilled personnel in technical fields on intelligent car and robot technology. They will gain practical ability as leaders, mastering their special fields, understanding the peripheral technologies, and leading research and development teams. The special-feature program of this course is seven comprehensive practicums as team learning. Internship students of national colleges of technology and other universities as well as master and PhD course students of these graduate schools participate in the practicums.

DOI： 10.4307/jsee.65.4_51

その他リンク： https://ci.nii.ac.jp/naid/130005966874

オントロジーに基づく自動運転向け意思決定システムの推論速度評価査読有り

橋本康平, 石田裕太郎, 三好竜平, 市瀬龍太郎, 我妻広明, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 33 ( 0 ) 681 - 684 2017年01月

Depth-Based Visual Servoing Using Low-Accurate Arm 査読有り

Hofer L., Tanaka M., Tamukoh H., Nassiraei A., Morie T.

Proceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016 524 - 531 2016年12月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2016 IEEE. This paper proposes a visual-servoing method dedicated to grasping of daily-life objects. In order to obtain an affordable solution, we use a low-accurate robotic arm. Our method corrects errors by using an RGB-D sensor. It is based on SURF invariant features which allows us to perform object recognition at a high frame rate. We define regions of interest based on depth segmentation, and we use them to speed-up the recognition and to improve reliability. The system has been tested on a real-world scenario. In spite of the lack of accuracy of all the components and the uncontrolled environment, it grasps objects successfully on more than 95% of the trials.

DOI： 10.1109/SCIS-ISIS.2016.0116

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85010441822&origin=inward

Lightness modification method considering craik-o'brien effect for protanopia and deuteranopia 査読有り

Bao S., Tanaka G., Tamukoh H., Suetake N.

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E99A ( 11 ) 2008 - 2011 2016年11月

記述言語：英語掲載種別：研究論文（学術雑誌）

Copyright © 2016 The Institute of Electronics, Information and Communication Engineers. Protanopes and deuteranopes are difficult to distinguish some color pairs. In this letter, a new lightness modification method which considers the Craik-O'Brien effect is proposed. The lightness modification is performed at parts which are difficult to distinguish in the protanopia or deuteranopia. Experiments show the validity of the proposed method.

DOI： 10.1587/transfun.E99.A.2008

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84994577827&origin=inward

Hardware-Oriented Algorithm for Deep Neural Networks Composed of Self-Organizing Maps 査読有り

Yuichiro Tanaka, Hakaru Tamukoh

Proc. of Int. Workshop on Smart Info-Media Systems in Asia 2016年09月

Hardware-Oriented Algorithm for Chaotic Boltzmann Machines 査読有り

Ichiro Kawashima, Seiji Uenohara, Takashi Kato, Masatoshi Yamaguchi, Hideyuki Suzuki, Takashi Morie, Hakaru Tamukoh

Proc. of Int. Workshop on Smart Info-Media Systems in Asia 2016年09月

Improvement of lightness modification method based on Craik-O'Brien effect for dichromats 査読有り

Bao S., Tanaka G., Tamukoh H., Suetake N.

2015 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2015 75 - 78 2016年03月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Dichromats is difficult to distinguish some color pairs. There is lightness modification method which considers the Craik-O'Brien effect. Although the method can obtain good results in many cases, there is a room for improvement. In this paper, a new lightness modification method which considers the Craik-O'Brien effect is proposed. In the proposed method, the lightness modification is mainly performed at parts which are difficult to distinguish for dichromats. The validity of the proposed method is shown by experiments using some images.

DOI： 10.1109/ISPACS.2015.7432741

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966586535&origin=inward

カーボンナノチューブ・ポリ酸ネットワークのニューロン発火特性のカオス解析査読有り

付凌翔, 田向権, 小川琢治, 田中啓文

応用物理学会学術講演会講演予稿集 ( 公益社団法人応用物理学会 ) 2016.1 ( 0 ) 2286 - 2286 2016年03月

Time-domain weighted-sum calculation for ultimately low power VLSI neural networks 査読有り

Wang Q., Tamukoh H., Morie T.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9947 LNCS 240 - 247 2016年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing AG 2016. Time-domain weighted-sum operation based on a spiking neuron model is discussed and evaluated from a VLSI implementation point of view. This calculation model is useful for extremely low-power operation because transition states in resistance and capacitance (RC) circuits can be used. Weighted summation is achieved with energy dissipation on the order of 1 fJ using the current CMOS VLSI technology if 1 GΩ order resistance can be used, where the number of inputs can be more than a hundred. This amount of energy is several orders of magnitude lower than that in conventional digital processors. In this paper, we show the software simulation results that verify the proposed calculation method for a 500-input neuron in a three-layer perceptron for digit character recognition.

DOI： 10.1007/978-3-319-46687-3_26

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84992665665&origin=inward

Restricted boltzmann machines without random number generators for efficient digital hardware implementation 査読有り

Hori S., Morie T., Tamukoh H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9886 LNCS 391 - 398 2016年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing Switzerland 2016. Restricted Boltzmann machines (RBMs) have actively been studied in the field of deep neural networks. RBMs are stochastic artificial neural networks that can learn a probability distribution of input datasets. However, they require considerable computational resources, long processing times and high power consumption due to huge number of random number generation to obtain stochastic behavior. Therefore, dedicated hardware implementation of RBMs is desired for consumer applications with low-power devices. To realize hardware implementation of RBMs in a massively parallel manner, each unit must include random number generators (RNGs), which occupy huge hardware resources. In this paper, we propose a hardware-oriented RBM algorithm that does not require RNGs. In the proposed method, as a random number, we employ underflow bits obtained from the calculation process of the firing probability. We have developed a software implementation of fixed-point RBMs to evaluate the proposed method. Experimental results show that a 16-bit fixed-point RBM can be trained by the proposed method, and the underflow bits can be used as random numbers in RBM training.

DOI： 10.1007/978-3-319-44778-0_46

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84987949104&origin=inward

FPGA implementation of autoencoders having shared synapse architecture 査読有り

Suzuki A., Morie T., Tamukoh H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9947 LNCS 231 - 239 2016年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing AG 2016. Deep neural networks (DNNs) are a state-of-the-art processing model in the field of machine learning. Implementation of DNNs into embedded systems is required to realize artificial intelligence on robots and automobiles. Embedded systems demand great processing speed and low power consumption, and DNNs require considerable processing resources. A field-programmable gate array (FPGA) is one of the most suitable devices for embedded systems because of their low power consumption, high speed processing, and reconfigurability. Autoencoders (AEs) are key parts of DNNs and comprise an input, a hidden, and an output layer. In this paper, we propose a novel hardware implementation of AEs having shared synapse architecture. In the proposed architecture, the value of each weight is shared in two interlayers between input-hidden layer and hidden-output layer. This architecture saves the limited resources of an FPGA, allowing a reduction of the synapse modules by half. Experimental results show that the proposed design can reconstruct input data and be stacked. Compared with the related works, the proposed design is register transfer level description, synthesizable, and estimated to decrease total processing time.

DOI： 10.1007/978-3-319-46687-3_25

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84992561479&origin=inward

A CMOS unit circuit using subthreshold operation of MOSFETs for chaotic Boltzmann machines 査読有り

Yamaguchi M., Kato T., Wang Q., Suzuki H., Tamukoh H., Morie T.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9947 LNCS 248 - 255 2016年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing AG 2016. Boltzmann machines are a useful model for deep neural networks in artificial intelligence, but in their software or hardware implementation, they require random number generation for stochastic operation, which consumes considerable computational resources and power. Chaotic Boltzmann machines (CBMs) have been proposed as a model using chaotic dynamics instead of stochastic operation. They require no random number generation, and are suitable for analog VLSI implementation. In this paper, we describe software simulation results for CBM operation, and propose a CMOS circuit of CBMs using the subthreshold operation of MOSFETs.

DOI： 10.1007/978-3-319-46687-3_27

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84992609437&origin=inward

自動運転のための知的処理におけるハードウェアアクセラレータ利用の基礎検討査読有り

石田裕太郎, 橋本康平, 有田裕太, 田中良道, 西田健, 伊藤太久磨, 井上秀雄, 通山恭一, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 32 ( 0 ) 49 - 50 2016年01月

MRCoHOG 特徴量を用いたハードウェア指向人物検出査読有り

山崎裕太, 大江至流, 鈴木, 久野和宏, 山田英夫, 榎田修一, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 32 ( 0 ) 53 - 56 2016年01月

A color quantization based on vector error diffusion and particle swarm optimization considering human visibility 査読有り

Kubota R., Tamukoh H., Kawano H., Suetake N., Cha B., Aso T.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9431 332 - 343 2016年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In this paper, we propose a new color quantization method for generation of the color-reduced images. The proposed method employs a vector error diffusion (VED) method and a particle swarm optimization (PSO). VED method based on Floyd-Steinberg dithering is used for display of the color-reduced image. Furthermore, a color palette used in VED method is optimized by PSO. PSO generates the effective color palette with evaluating a human visibility of the color-reduced image on the display. The validity and the effectiveness of the proposed method are confirmed by some experiments.

DOI： 10.1007/978-3-319-29451-3_27

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84959020337&origin=inward

VLSI Pulse-Coupled Phase Oscillator Networks toward Spike-based Computation 招待有り査読有り

Hakaru Tamukoh and Takashi Morie

The 2nd International Workshop on Brain-Inspired Information Communication Technologies (BIICT), 9th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT) 2015年12月

Measurement and Analysis of a CMOS Chaotic Spiking Oscillator Circuit That Acts as a Filter of Spike Trains 査読有り

Masatoshi Yamaguchi, Seiji Uenohara, Hakaru Tamukoh, and Kazuyuki Aihara

Proc. of the 2015 Int. Symp. on Nonlinear Theory and its Applications (NOLTA2015) 515 - 518 2015年12月

VLSI Pulse-Coupled Phase Oscillator Networks and Their Emulator toward Spike-based Computation for Intelligent Processing 招待有り査読有り

Hakaru Tamukoh, Yasuhiro Suedomi, Kenji Matsuzaka, and Takashi Morie

Extended Abstracts of the 2015 (47th) International Conference on Solid State Devices and Materials (SSDM2015) 806 - 807 2015年09月

A-2-15 カオスボルツマンマシンのCMOS回路化(A-2.非線形問題,一般セッション) 査読有り

加藤孝史, 上ノ原誠二, 鈴木秀幸, 田向権, 森江隆

電子情報通信学会基礎・境界ソサイエティ/NOLTAソサイエティ大会講演論文集 ( 一般社団法人電子情報通信学会 ) 2015 2015年08月

A-2-16 大規模非線形結合系のためのデバイスミスマッチ補償回路の評価(A-2.非線形問題,一般セッション) 査読有り

上ノ原誠二, 森江隆, 田向権, 合原一幸

電子情報通信学会基礎・境界ソサイエティ/NOLTAソサイエティ大会講演論文集 ( 一般社団法人電子情報通信学会 ) 2015 2015年08月

Parameterized Digital Hardware Design of Pulse-coupled Phase Oscillator Networks 査読有り

Yasuhiro Suedomi, Hakaru Tamukoh, Kenji Matsuzaka, Michio Tanaka, and Takashi Morie

Neurocomputing in press 2015年04月

クレイク・オブライエン効果の簡略型生成法による二色覚のためのコントラスト改善 (スマートインフォメディアシステム) 査読有り

田向権, 田中豪, 末竹規哲

電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 ( 一般社団法人電子情報通信学会 ) 114 ( 496 ) 39 - 44 2015年03月

担当区分：筆頭著者記述言語：日本語掲載種別：研究論文（学術雑誌）

本稿では,クレイク・オブライエン効果を生成する簡略型アルゴリズムの提案を行う.提案方法では,入力画像中のオブジェクトの周辺部のみ函南のY成分を変化させることでクレイク・オブライエン効果を生成する.これにより,オブジェクト間のコントラストに差がつくような錯視が生成され,入力画像の色に関する印象を保ったまま,2色覚者にとって弁別困難なオブジェクトを判別可能とする.提案手法では,オブジェクトの輪郭情報を抽出するためにTモデルフィルタを導入する.Tモデルフィルタは1ライン分のラインバッファで構成可能であるため,少ないメモリ使用量での実現が可能である.また,提案手法では,乗算や除算を必要としない簡略化が成されている.これらにより,組込み化に適したクレイク・オブライエン効果の生成が可能となる.いくつかの実験により提案手法の有効性を示す.

その他リンク： http://ci.nii.ac.jp/naid/110010017514

A Multidimensional Configurable Processor Array - Vocalise 査読有り

Jiang Li, Yusuke Atsumari, Hiromasa Kubo, Yuichi Ogishima, Satoru Yokota, Hakaru Tamukoh, and Masatoshi Sekine

IEICE Transaction on Information and Systems E98-D ( 2 ) 313 - 324 2015年02月

パルス結合位相振動子ネットワークのディジタル回路実装とその応用 (ニューロコンピューティング) 査読有り

田向権, 末富康寛, 松坂建治, 田中宙夫, 森江隆

電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 ( 一般社団法人電子情報通信学会 ) 114 ( 437 ) 15 - 20 2015年01月

担当区分：筆頭著者記述言語：日本語掲載種別：研究論文（学術雑誌）

本稿では,パルス結合位相振動子ネットワークのディジタル回路実装と,その領域ベース結合Markov random field (MRF)モデルへの応用を提案する.提案回路は,パラメタ化されたVerilog-HDLにより記述され,ネットワークサイズ,結合方法,振動子内部関数の形状,演算精度などをパラメタにより変更可能である.この機能により,専用LSI実装のための様々な大規模ネットワークのエミュレーションが可能となる.提案回路の論理シミュレーションでは,2振動子の相互結合における基本動作である同相,逆相同期現象を確認するとともに,提案回路を用いて領域ベース結合MRFモデルを実装し,2次元信号の領域分割処理に応用可能なことを示した.

その他リンク： http://ci.nii.ac.jp/naid/110010008600

Self-Learning of Feature Regions for Image Recognition 査読有り

Satoru Yokota, Jiang Li, Yuichi Ogishima, Hiromasa Kubo, Hakaru Tamukoh, and Masatoshi Sekine

Journal of Computer Sciences and Applications 3 ( 1 ) 1 - 10 2015年01月

A CMOS circuit for PWM-mode nonlinear transformation robust to device mismatches to implement coupled map lattice models 査読有り

Uenohara Seiji, Atuti Daisuke, Matsuzaka Kenji, Tamukoh Hakaru, Morie Takashi, Aihara Kazuyuki

Nonlinear Theory and Its Applications, IEICE ( 一般社団法人電子情報通信学会 ) 6 ( 4 ) 570 - 581 2015年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

In order to develop large-scale nonlinear dynamical systems using CMOS integrated circuits, we propose a core circuit for coupled map lattice (CML) models. The characteristics of the core circuits in the lattice on a chip are not generally equal, which is caused by CMOS device mismatches, including parasitic capacitance and wiring resistance. The proposed circuit solves this problem; it compensates for a DC offset voltage variation by holding it at a capacitor, and also for current variation by adjusting the bias voltage of a current source automatically so as to bring the current close to a target value. The proposed core circuit has been designed and fabricated using TSMC 0.25 µm CMOS technology. The measurement results using the fabricated circuit have shown that the bit precision is more than 8 bits, even if there is a DC offset voltage of 100 mV or a bias-voltage change of 100 mV in a switched current source.

DOI： 10.1587/nolta.6.570

その他リンク： http://ci.nii.ac.jp/naid/130005102329

Morphological Associative Memory Employing a Split Store Method 査読有り

Hakaru Tamukoh, Kensuke Koga, Hideaki Harada, and Takashi Morie

21th Int. Conf. on Neural Information Processing (ICONIP2014), (Lecture Notes in Computer Science, LNCS 8836) III-341 - III-348 2014年10月

Simplified Lightness Modification Algorithm for Generating Craik O’Brien Effect 査読有り

Hakaru Tamukoh, Go Tanaka, Noriaki Suetake

Int. Workshop on Smart Info-Media Systems in Asia (SISA2014) 54 - 57 2014年10月

A Partice Swarm Optimization Using Combined Information on Personal Best Positions 査読有り

Ryosuke Kubota, Hakaru Tamukoh

ICIC Express Letters 8 ( 8 ) 2069 - 2074 2014年08月

A particle swarm optimization using combined information on personal best positions 査読有り

Kubota R., Tamukoh H.

ICIC Express Letters 8 ( 8 ) 2069 - 2074 2014年08月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Particle swarm optimization (PSO), which is included in swarm intelligence, is a population-based stochastic optimization technique. The motivation of the PSO is based on social behavior of fish schooling or bird flocking. The PSO is attractive due to the simplicity of its concept and the facility for the applications to diverse optimization problems. However, the traditional PSO cannot work in high dimensional searching space effectively. In this paper, we propose a new PSO considering a combined component, which is obtained from the personal best positions of the particles in the searching space. The present PSO first calculates a center of the personal best positions weighted by their fitness values, and uses it for the update of the particle position. The effectiveness and the validity of the proposed PSO are verified by applying it to some benchmarks of the continuous variable optimization problems. © 2014 ICIC International.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84903462000&origin=inward

Object Segmentation Using 3D Information Obtained from RGB-D Sensor 査読有り

Toshihiro Matsumoto, Michio Tanaka, Hakaru Tamukoh, and Takashi Morie

2014 RISP Int. Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2014) 377 - 380 2014年03月

Sound Quality Improvement of Low Bit Rate Coded Audio Signals with Nonlinear Functions 査読有り

Tetsutaro Yoshino, Yasushi Sato, Hakaru Tamukoh, and Takashi Morie

2014 RISP Int. Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2014) 237 - 240 2014年03月

Robustness to CMOS Device Mismatches of Time-domain Voltage/Current Sampling Circuits for Implementing Large-scale Coupled Nonlinear Dynamical Systems 査読有り

Seiji Uenohara, D. Atuti, Kenji Matsuzaka, Hakaru Tamukoh, and Takashi Morie

2014 RISP Int. Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2014) 93 - 96 2014年03月

Digital Circuit Design of Pulse-coupled Phase Oscillator Systems for Coupled MRF Models 査読有り

Yasuhiro Suedomi, Hakaru Tamukoh, Michio Tanaka, Kenji Matsuzaka, and Takashi Morie

2014 RISP Int. Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2014) 89 - 92 2014年03月

Fuzzy-Rule-Embedded Reduction Image Construction Method for Image Enlargement with High Magnification 査読有り

Hakaru Tamukoh, Hideaki Kawano, Noriaki Suetake, Ryosuke Kubota, Byungki Cha and Takashi Aso

Proc. of the 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2014) 228 - 233 2014年01月

Morphological associative memory employing a split store method 査読有り

Tamukoh H., Koga K., Harada H., Morie T.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8836 341 - 348 2014年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© Springer International Publishing Switzerland 2014. The morphological associative memory (MAM) has advantages of large memory capacity and high perfect recall rate in comparison with other associative memory models. However, the MAM cannot store a large data in memory matrices M and W because the space complexity of the ordinary method is O(n 2 ) when the dimension of input data is n. In this paperCwe propose a MAM employing a split store method. The proposed method splits a given stored pattern into √n sub-pattern, then memory matrices are independently generated for each sub-pattern. Experimental results show that the perfect recall rate and CPU time of the proposed method are nearly equal to the ordinary method while the proposed method reduces the space complexity to O(n 1.5 ).

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84909992911&origin=inward

Fuzzy-rule-embedded reduction image construction method for image enlargement with high magnification 査読有り

Tamukoh H., Suetake N., Kawano H., Kubota R., Cha B., Aso T.

VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications 1 228 - 233 2014年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This paper proposes a fuzzy-rule-embedded reduction image construction method for image enlargement. A fuzzy rule is generated by considering distribution of pixel value around a target pixel. The generated rule is embedded into the target pixel in a reduction image. The embedded fuzzy rule is used in a fuzzy inference to generate a highly magnified image from the reduction image. Experimental results, which scale factors are three and four, show that the proposed method realizes high-quality image enlargement in terms of both objective and subjective evaluations in comparison with conventional methods. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84906895283&origin=inward

ニューラルネットワークのハードウェア実装とそのシステム化へのアプローチ

田向権, 関根優年

日本神経回路学会誌 ( 日本神経回路学会誌 ) 20 ( 4 ) 166 - 173 2013年12月

A PWM-mode CMOS Coupled Map Lattice Circuit Robust to Device Mismatches 査読有り

Seiji Uenohara, D. Atuti, Kenji Matsuzaka, Hakaru Tamukoh, Takashi Morie, and Kazuyuki Aihara

The 3rd Int. Symp. on Innovative Mathematical Modelling 125 - 125 2013年11月

Design of Networked hw/sw Complex System using Hardware Object Model and Its Application 査読有り

Hakaru Tamukoh and Masatoshi Sekine

Proc. of 39th Annual Conference of the IEEE Industrial Electronics Society (IECON2013) 2250 - 2255 2013年11月

Parameterized Digital Hardware Design of Pulse-coupled Phase Oscillator Model toward Spike-based Computing 査読有り

Yasuhiro Suedomi, Hakaru Tamukoh, Michio Tanaka, Kenji Matsuzaka, and Takashi Morie

Proc. 20th Int. Conf. on Neural Information Processing (ICONIP2013), (Lecture Notes in Computer Science, LNCS 8228) Ⅲ-17 - Ⅲ-24 2013年11月

A PWM-Mode CMOS Threshold-Coupled-Map Circuit Robust to Device Mismatches 査読有り

Seiji Uenohara, D. Atuti, Kenji Matsuzaka, Hakaru Tamukoh, Takashi Morie, and Kazuyuki Aihara

Abs. Collection of the 2013 Int. Symp. on Nonlinear Theory and its Applications (NOLTA2013) 503 - 506 2013年09月

Bandwidth Extension of Low Bit Rate Coded Audio Signals with Distortion Functions

Tetsutaro Yoshino, Yasushi Sato, Hakaru Tamukoh, and Takashi Morie

Proc. of the 13th Kyutech-Postech Joint Workshop on Neuroinformatics 23 - 24 2013年08月

A CMOS LSI Pulse-Coupled Phase Oscillator System

Yuya Miyadai, Kenji Matsuzaka, Takashi Tohara, Michio Tanaka, Hakaru

Proc. of the 13th Kyutech-Postech Joint Workshop on Neuroinformatics 21 - 22 2013年08月

Object Segmentation Using 3D Information Obtained from RGB-D Sensor

Toshihiro Matsumoto, Michio Tanaka, Hakaru Tamukoh, and Takashi Morie

Proc. of the 13th Kyutech-Postech Joint Workshop on Neuroinformatics 19 - 20 2013年08月

FPGA Implementation of Pulse-Coupled Phase Oscillators for Spike-Based Computing

Yasuhiro Suedomi, Hakaru Tamukoh, Michio Tanaka, Kenji Matsuzaka, and Takashi Morie

Proc. of the 13th Kyutech-Postech Joint Workshop on Neuroinformatics 17 - 18 2013年08月

Associative Memory Operation Using a Spiking Neural Network LSI System

Yuya Kobayashi, Michitaka Maeda, Hakaru Tamukoh, Haichao Liang, Kenji

Proc. of the 13th Kyutech-Postech Joint Workshop on Neuroinformatics 15 - 16 2013年08月

Lightness Modification Method to Increase Visibility of Dichromats

Kohei Hidaka, Hakaru Tamukoh, Noriaki Suetake

Proc. of the 13th Kyutech-Postech Joint Workshop on Neuroinformatics 33 - 34 2013年08月

Morophological Associative Memory Model to Recall Inclusion Pattern

Kensuke Koga, Hidetaka Harada, Hakaru Tamukoh

Proc. of the 13th Kyutech-Postech Joint Workshop on Neuroinformatics 31 - 32 2013年08月

Hardware/Software Complex System and Its Application 招待有り

Hakaru Tamukoh

Proc. of the 13th Kyutech-Postech Joint Workshop on Neuroinformatics 4 - 6 2013年08月

Fast image-enlargement algorithm for the augmentation of the high-frequency component by employing a hierarchical predefined codebook 査読有り

Tamukoh H., Kawano H., Suetake N., Cha B., Aso T.

International Journal of Innovative Computing, Information and Control 9 ( 2 ) 903 - 914 2013年05月

記述言語：英語掲載種別：研究論文（学術雑誌）

High-quality image enlargement is achieved by the augmentation of the high-frequency components that deteriorate as a result of image enlargement. A codebook-based method, which utilizes the relationship of the low- and high-frequency image components, is a proven method of high-quality image enlargement. However, this method requires a significant calculation time to be employed for a codebook search process, and thus is not suitable for real-time processing. In this paper, we propose a fast image-enlargement algorithm that includes a hierarchical predefined codebook and a fast edge-patch detection rule. A hierarchical codebook search reduces the number of comparisons that need to be made in comparison with a conventional full codebook search. In addition, applying the augmentation of the high-frequency components to only the edge patches detected under the proposed rule reduces the calculation time. The experimental results show that the calculation time of the proposed method is around 180 times faster than that of the fastest existing codebook-based method. The proposed method realizes high-quality image enlargement in terms of both objective and subjective evaluations in comparison with conventional methods. © 2013 ICIC International.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84877792007&origin=inward

A TCP/IP Hardware Stack Directly Connectable to WEB Application Circuit 査読有り

Kotoko Fujita, Nadav Bergstein, Hakaru Tamukoh and Masatoshi Sekine

Proc. of IEEE Symposium on Low-Power and High-Speed Chips (COOL Chips XVI) 2013年04月

画像音声認識・音声合成・並列演算・画像通信における論理回路による実装例

関根優年, 田向権

電子情報通信学会誌 ( 電子情報通信学会 ) 96 ( 2 ) 2013年02月

Fast Image-Enlargement Algorithm for the Augmentation of the High-Frequency Component by Employing a Hierarchical Predefined Codebook 査読有り

Hakaru Tamukoh, Hideaki Kawano, Noriaki Suetake, Byungki Cha, and Takashi Aso

International Journal of innovative Computing Information and Control 9 ( 2 ) 903 - 914 2013年02月

A Modified Prticle Swarm Optimization Considering Component Combined with Personal Best Positions 査読有り

Ryosuke Kubota and Hakaru Tamukoh

Proc. of 7th WSEAS International Conference on Circuits, Systems, Signal and Telecommunications (CSST '13) 2013年01月

A Data Embedded Reduction Image Generation Method for High-Quality Image Enlargement 査読有り

Hakaru Tamukoh, Hideaki Kawano, Noriaki Suetake, Masatoshi Sekine, Byungki Cha, and Takashi Aso

Proc. of 7th WSEAS International Conference on Circuits, Systems, Signal and Telecommunications (CSST '13) 2013年01月

Hardware/Software Complex System Model for Brain Process by Configurable Circuits 査読有り

Masatoshi Sekine, Hakaru Tamukoh, and Jiang Li

International Journal on Computing 3 ( 1 ) 1 - 7 2013年01月

Brain Process: Hardware/Software Complex System Using Logic Circuits in FPGA Array Named Vocalise 査読有り

Masatoshi Sekine, Hakaru Tamukoh, J. Li, K. Manabe, T. Suzuki, K. Okamot, B. Ogasawara, R. Uegaki, K. Fujita, Y. Atsumari

Journal of Procedia Engineering 50 253 - 264 2012年10月

Live demonstration: Internet Booster a novel WEB application platform accelerated by reconfigurable virtual hardware circuits 査読有り

Tamukoh H., Bergstein N., Fujita K., Sekine M.

ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems 2012年09月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This live demonstration presents an Internet Booster (IB) which is a novel WEB application platform accelerated by virtual hardware circuits. Akey-idea of IB is obtaining relevant WEB applications virtual hardware circuit through the Internet. The downloaded virtual hardware circuit is dynamically reconfigured into an FPGA and cooperates with software and network to accelerate WEB application. In order to realize the concept of IB, we develop a hwModule VC FPGA Cardbus board and a networked hw/sw complex system to access virtual hardware circiuts via the Internet. Furthermore, we also develop a virtual hardware video codec and a TCP/IP hardware stack to construct a demonstration system. In the live demonstration, we show a WEB streaming application accelerated by IB on the local area network environment. The demonstration system achieves full-color VGA size (24 bits RGB, 640 × 480 pixels) video streaming with around 20 frames per second. © 2012 IEEE.

DOI： 10.1109/ISCAS.2012.6272135

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84866605736&origin=inward

A look-Up Table-based Lightness Modification for Increasing Visibility of Dichromats 査読有り

Hakaru Tamukoh, Masatoshi Sekine, Go Tanaka and Noriaki Suetake

Proc. of Int. Workshop on Smart Info-Media Systems in Asia (SISA2012) 39 - 42 2012年09月

Hardware Accelerated WEB Platform based on FPGA Array Server and Mobile FPGA Card 査読有り

Jiang Li, Hakaru Tamukoh and Masatoshi Sekine

Proc. of 3rd International Conference on Internet Technology and Applications (iTAP2012) 4 in CD-ROM 2012年08月

Live Demonstration: ``Internet Booster" A Novel WEB Application Platform Accelerated by Reconfigurable Virtual Hardware Circuits 査読有り

Hakaru Tamukoh, Nadav Bergstein, Kotoko Fujita and Masatoshi Sekine

Proc. of IEEE International Symposium on Circuit and Systems (ISCAS2012) 76 2012年05月

2D/3D FPGA Array for Brain Process and Numerical Computation 査読有り

Jiang Li, Kenichi Takahashi, Hakaru Tamukoh and Masatoshi Sekine

Proc. of 8th International Conference on Natural Computation (ICNC'12) 16 - 19 2012年05月

Distributed Computing Circuits in Scalable 2D/3D FPGA Array for 2D/3D Poisson Equation Problem 査読有り

Jiang Li, Kenichi Takahashi, Hakaru Tamukoh and Masatoshi Sekine

Proc. of IEEE Symposium on Low-Power and High-Speed Chips COOL Chips XV 2 in CD-ROM 2012年04月

An FPGA Implementation of Hierarchal Predefined Codebook based Image Enlargement 査読有り

Hakaru Tamukoh, Hideaki Kawano, Noriaki Suetake, Masatoshi Sekine, Byungki Cha, and Takashi Aso

Proc. of Int. Workshop on Advanced Image Technology 622 - 625 2012年01月

High-Performance WEB Application Accelerated by Networked hw/sw Complex System and Virtual Hardware Circuit 招待有り査読有り

Hakaru Tamukoh, Masatoshi Sekine

Int. Symposium on Semiconductor Manufacturing Intelligence 6 in CD-ROM 2012年01月

Rough winner-take-all for hardware oriented vector quantization algorithm 査読有り

Tamukoh H., Horio K., Yamakawa T., Sekine M.

IEICE Electronics Express 8 ( 11 ) 773 - 779 2011年06月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（学術雑誌）

In this paper, we propose a hardware oriented vector quantization algorithm employing rough-winner-take-all neural network. The proposed algorithm is almost same as K-means clustering which is the simplest vector quantization. The only different point is that the proposed method employs rough-winner-take-all as the substitute of ordinary winner-take-all. In a rough-winner-take-all strategy, the winner is roughly selected in the early learning stage and is strictly assigned in the later stage. The simulation results show that the quantization performance of the proposed method is nearly equal to Neural Gas which is an excellent vector quantization. Besides, the proposed method can be realized as an extra mode of existing K-means or Self- Organizing Map hardware by changing its winner-take-all controlling. © IEICE 2011.

DOI： 10.1587/elex.8.773

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79958822511&origin=inward

Rough-winner-take-all for Hardware Oriented Vector Quantization Alogrithm 査読有り

Hakaru Tamukoh, Keiichi Horio, Takeshi Yamakawa and Masatoshi Sekine

IEICE Electronics Express 8 ( 11 ) 773 - 779 2011年06月

Internet booster: A networked hw/sw complex system and its application to hi-performance web application 査読有り

Tamukoh H., Hanai K., Kurogi R., Matsushita S., Watanabe M., Kobayashi Y., Sekine M.

2010 World Automation Congress, WAC 2010 2010年12月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

An internet booster (IB) based on a networked hw/sw complex system is presented. The key-idea of IB is obtaining relevant WEB applications virtual hardware circuit through internet; just as same as downloading general WEB applications specific software from the server. The downloaded virtual hardware circuit is realized into an FPGA and cooperates with server's application which is also constructed with FPGA. In order to realize IB, the networked hw/sw complex system is proposed which provides remote FPGA reconfiguration and handshake mechanism between FPGAs over the internet. In this paper, we implement WEB application as IB; where the WEB application performs real time compression, transferring and extension of video. Experimental results show that we succeed in downloading virtual hardware circuit, dynamic reconfiguration and remote operation over the internet. Besides, we also achieved full-color VGA size (24bits RGB, 640x480 pixels) video streaming with 20 frames per second using a wavelet transfer based virtual video codec. The result leads that the proposed networked hw/sw complex system and IB can provide a new WEB application program environment which features both the hispeed of hardware and the flexibility of software. © 2010 TSI Press.