研究者詳細 - 田中　悠一朗

論文 - 田中　悠一朗

分割表示 >> ／全件表示55 件中 1 - 55 件目

Prediction of Timing and Amount of Houseplants Watering by an Echo State Network on Jetson 査読有り国際誌

Yoshimura W., Arimura K., Kobayashi R., Mizutani A., Fujino T., Tanaka Y., Sudo T., Ishizuka N., Ito K., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 529 - 534 2025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）

Cultivating of houseplants in biophilic designed spaces requires appropriate timing and amount of watering. However, determining them is challenging, as fluctuations in ambient temperature can influence these factors. We develop a system capable of predicting ambient temperature changes and determining the appropriate timing and amount of watering. The system acquires ambient data using sensors connected to a Jetson Nano and processes the data using a neural network for the prediction and determination. We adopt an echo state network, a lightweight neural network, enabling a power-efficient system capable of running on edge devices. Additionally, we implement a function to notify the user of the timing and amount of watering via a chat service whenever the soil moisture content drops below a predefined threshold.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219505871&origin=inward

Lightweight Hand-Waving Action Recognition Using Reservoir Computing in a Cafeteria Environment 査読有り

Isomoto K., Fumoto S., Kobayashi R., Tanaka Y., Tamukoh H.

2025 IEEE/SICE International Symposium on System Integration, SII 2025 1034 - 1040 2025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）

Owing to the global labor shortage and increasing need for operational efficiency, the adoption of service robots is advancing rapidly. These robots must recognize human action to understand human intention and respond appropriately based on that understanding. The action recognition systems embedded in robots need to be lightweight to operate efficiently with limited computational resources. Reservoir computing (RC) is one of the solutions for lightweight action recognition systems. Yamaguchi et al. proposed an RC-based hand-waving recognition system; however, the system cannot process multiple persons simultaneously and works only when one person is in the image. Therefore, this study proposes a lightweight hand-waving recognition system that integrates OpenPose, StrongSORT, and RC to work in complex environments with multiple individuals. Experimental results demonstrated the effectiveness of the proposed system in processing multiple people simultaneously in a crowded environment and accurately recognizing hand-waving actions with 90.75% accuracy. We also confirmed that the proposed system can process data at 24-26 FPS. We demonstrated that the proposed system can perform real-time processing. In addition, the robot with the proposed system recognized hand-waving actions in the "Restaurant"task of RoboCup@Home 2024 and obtained the second-place score.

DOI： 10.1109/SII59315.2025.10871098

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=86000251022&origin=inward

Classification of Human Activity by Spiking Neural Networks using Event-based Vision Sensors 査読有り

Rakheja A., Saini R., Toyoda R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 484 - 486 2025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 presents a human action classification system using a spiking neural network (SNN) and an event-based vision sensor (EVS). The EVS captures asynchronous data with high temporal resolution, wide dynamic range, and motion blur resistance. SNNs, inspired by biological neurons, process this data event-driven, ensuring energy efficiency, low latency, and scalability. Using a custom EVS dataset of 600 videos across four action types and the optical flow for feature extraction, the system achieved 93% accuracy, offering an efficient solution for action recognition.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219530000&origin=inward

Classification of Human Activity by Event-based Vision Sensors using Echo State Networks 査読有り国際誌

Saini R., Rakheja A., Toyoda R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 476 - 478 2025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 system for human activity recognition using an event-based vision sensor (EVS) with echo state networks (ESNs). Conventional cameras are susceptible to motion blur and require computationally intensive methods, whereas EVS provides no motion blur and low latency. Our research aims to enable accurate recognition of human activities by using energy-efficient methods. Therefore, we adopt ESNs, which require low computational costs, for the classifier. Additionally, we use feature extraction algorithms such as optical flow and histogram of gradients to improve accuracy. We used an EVS activity recognition dataset created by us containing six human activities and 600 videos. The results showed that our hybrid approach outperformed several techniques. We achieved 89% accuracy when trained with ridge regression.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219518069&origin=inward

Application of AI Robot Technology for Biophilic Design 査読有り国際誌

Manabe K., Miyazono R., Ito K., Sudo T., Ishizuka N., Mizutani A., Tamukoh H., Tanaka Y., Ueda E., Anamizu Y., Furuya L., Tamai H., Nakano S., Tanaka H.

Proceedings of International Conference on Artificial Life and Robotics 513 - 518 2025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）

To enhance human health and well-being, Biophilic design has been increasingly recognized in recent years. This design is characterized by the integration of natural elements such as plants, nature light, and water into spaces. However, challenges are posed by the maintenance of live plants, as their decline can be caused by insufficient environmental conditions. The aim of this study is to propose a robotic system of autonomously relocate houseplants to environments optimized based on sensor data, including light, temperature, and humidity. Through the integration of AI robotics with ecological design principles, it is aimed to enhance sustainability and redefine the relationship between humans, nature, and technology, fostering a harmonious interaction among "robots, nature, and humans.".

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219571283&origin=inward

An Exhibition Environment with 2D Markers for Guide Robot 査読有り国際誌

Mizutani A., Hattori Y., Tanaka Y., Ishizuka N., Tanaka H., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 524 - 528 2025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）

The guide robot in an exhibition environment is expected to entertain visitors and reduce the maintenance cost of updating the robot database according to the changes in exhibition contents. The exhibition comprises printed panels for humans and a 2D marker printed with ultraviolet ink for robots. 2D markers are attached to the bottom of the exhibition furniture to make the markers invisible to visitors. The exhibition point may change depending on the contents of the exhibition. By searching for 2D markers autonomously in an exhibition space, the robot can update its internal database. This enables us to keep both the printed panel and the robot database without changing the robot database by users. The experiment is conducted in an exhibition environment, and the success rate in finding the exhibition point is evaluated.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219566021&origin=inward

Simple open-set recognition combining metric learning and anomaly detection 査読有り国際誌

Kanaoka D., Tanaka Y., Tamukoh H.

Advanced Robotics 2025年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

In real world scenarios, there is always a risk that an object recognition system will encounter unknown objects. Open-set recognition, which identifies such objects as ‘unknown,’ is a crucial research area. Most existing open-set recognition methods have complex structures. Considering the application of closed-set recognition models already used in the real world are utilized for open-set recognition, a simple structure is preferable for implementation. In this study, we propose a novel open-set recognition method that focuses on the feature space of a recognition model by combining metric learning and anomaly detection (AD). By applying metric learning, the feature vectors of the same class are clustered and those of different classes are separated, increasing the open space for the detection of unknown objects. The AD model trains on feature vectors from the intermediate layers of a recognition model, allowing unknown detection without altering the existing model structure. We evaluated various combinations of metric learning and AD techniques, identifying an effective pairing. Our results show that the proposed method performs comparably to state-of-the-art approaches on simple datasets. The simplicity of the proposed method makes it easy to adapt existing closed-set recognition models for open-set recognition in real-world applications.

DOI： 10.1080/01691864.2025.2486394

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

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10月

記述言語：英語掲載種別：研究論文（学術雑誌）

<p>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.</p>

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

ナノマテリアルランダムネットワークを利用した物理リザバー演算素子とそのセンシング応用招待有り査読有り

田中啓文, アズハリサマン, バナジーディープ, 君塚紘喜, 田中悠一朗, 宇佐美雄生

表面と真空 ( 公益社団法人日本表面真空学会 ) 67 ( 9 ) 428 - 432 2024年09月

記述言語：日本語掲載種別：研究論文（学術雑誌）

<p>The rapid advancement of software-based deep learning has led to a surge in AI applications, yet hardware limitations in silicon CMOS technology hinder performance. Consequently, interest is growing in hardware technologies and new materials for artificial neural networks (ANNs) and neuromorphic systems. Exploiting nanomaterials nonlinearity caused by spontaneous physical phenomena holds promise of reducing power consumption in AI hardware. Reservoir computing devices, derived from recurrent neural networks, play a crucial role, with material reservoir devices showcasing “material intelligence.” Tailored nanomaterials for reservoir devices hold potential for revolutionizing AI, especially in robotics. As research progresses, focusing on device functionalization and applications, recent findings underline the significance of integrating nanomaterials into AI hardware for enhanced computational capabilities and energy efficiency.</p>

DOI： 10.1380/vss.67.428

その他リンク： https://www.jstage.jst.go.jp/article/vss/67/9/67_20181334/_pdf

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

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

Proceedings of the International Joint Conference on Neural Networks 2024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）

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

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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 ) 941 - 944 2024年06月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

<p>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.</p>

DOI： 10.1587/transfun.2023eal2050

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

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 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

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05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）

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

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

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

Proceedings of International Conference on Artificial Life and Robotics 421 - 424 2024年02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 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

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

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02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）

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.

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

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

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

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

Thermally Stable Ag<inf>2</inf>Se Nanowire Network as an Effective In-Materio Physical Reservoir Computing Device 査読有り国際誌

Kotooka T., Lilak S., Stieg A.Z., Gimzewski J.K., Sugiyama N., Tanaka Y., Kawabata T., Karacali A., Tamukoh H., Usami Y., Tanaka H.

Advanced Electronic Materials 10 ( 12 ) 2024年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

The artificial intelligence (AI) paradigm shifts from software to implementing general-purpose or application-specific hardware systems with lower power requirements. This study explored a material physical reservoir consisting of a material random network, called in-materio physical reservoir computing (RC) to achieve efficient hardware systems. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions as reservoir nodes, demonstrated the requisite characteristics of an in-materio physical reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. The power consumption of the in-materio physical reservoir is 0.07 nW per nanojunctions, confirming its highly efficient information processing system. As a hardware reservoir, the devices successfully performed waveform generation tasks. Finally, a voice classification by an in-materio physical reservoir is achieved over 80%, comparable to an RC software simulation. In-materio physical RC with rich nonlinear dynamics has huge potential for next-generation hardware-based AI.

DOI： 10.1002/aelm.202400443

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85210391008&origin=inward

Self-Organizing Multiple Readouts for Reservoir Computing 査読有り国際誌

Tanaka Y., Tamukoh H.

IEEE Access 11 138839 - 138849 2023年12月

担当区分：筆頭著者,　責任著者記述言語：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

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

Yoshioka K., Tanaka Y., Tamukoh H.

Proceedings - International Conference on Field-Programmable Technology, ICFPT 170 - 178 2023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）

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

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

Yoshioka K., Tanaka Y., Tamukoh H.

Proceedings - International Conference on Field-Programmable Technology, ICFPT 266 - 267 2023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）

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

Ensemble Learning of Multiple Readouts for Reservoir Computing 査読有り国際誌

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：筆頭著者,　責任著者記述言語：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

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

Kosei Isomoto, Yuga Yano, Yuichiro Tanaka, Hakaru Tamukoh

2023 International Workshop on Smart Info-Media Systems in Asia (SISA 2023) 30 - 33 2023年08月

FPGA Implementation of a Chaotic Boltzmann Machine Annealer 査読有り国際誌

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）

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

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

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

Proceedings of the International Joint Conference on Neural Networks 2023-June 2023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）

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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85169554268&origin=inward

In-material reservoir implementation of reservoir-based convolution 査読有り国際誌

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

Proceedings - IEEE International Symposium on Circuits and Systems 2023-May 2023年05月

担当区分：筆頭著者,　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 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

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02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）

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

Random network device fabricated using Ag2Se nanowires for data augmentation with binarized convolutional neural network 査読有り国際誌

Takumi Kotooka, Yuichiro Tanaka, Hakaru Tamukoh, Yuki Usami, Hirofumi Tanaka

Applied Physics Express 16 ( 1 ) 2023年01月

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

Material computing based on electrochemical dynamics 査読有り

Yuki Usami, Yuichiro Tanaka, Takumi Kotooka, Hakaru Tamukoh, Hirofumi Tanaka

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

Robust Trash Can Lid Opening System 査読有り

Kosei Isomoto, Yuga Yano, Yuichiro Tanaka, Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 46 - 46 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, Takashi Morie

The 4th International Symposium on Neuromorphic AI Hardware 44 - 44 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, Terumasa Tokunaga

The 4th International Symposium on Neuromorphic AI Hardware 42 - 42 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, Hakaru Tamukoh

The 4th International Symposium on Neuromorphic AI Hardware 28 - 28 2022年12月

Reservoir-based 1D convolution 査読有り

Yuichiro Tanaka, Hakaru Tamukoh,

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

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) 2022年06月

Reservoir-based convolution 査読有り国際誌

Yuichiro Tanaka, Hakaru Tamukoh

Nonlinear Theory and Its Applications, IEICE ( IEICE ) 13 ( 2 ) 397 - 402 2022年04月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

<p>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.</p>

DOI： 10.1587/nolta.13.397

その他リンク： http://hdl.handle.net/10228/00009117

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

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02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 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

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

Daiju Kanaoka, Yuichiro Tanaka, Hakaru Tamukoh

17th International Conference on Computer Vision Theory and Applications (VISAPP2022) 5 359 - 365 2022年02月

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

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

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

Yuichiro Tanaka, Hakaru Tamukoh

The 2021 NonLinear Science Workshop NLSW-34 2021年12月

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

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

ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding 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 brain-inspired neural network model consisting of the hippocampus, prefrontal cortex, and amygdala models for a navigation system that acquires specific knowledge in home environments from few experiences. The proposed model was evaluated in a home environment using a robot simulator. In the experiment, the robot determines a path for navigation based on the knowledge acquired by the brain-inspired model.

DOI： 10.1109/ISPACS51563.2021.9651058

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124147021&origin=inward

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

Kanaoka D., Tanaka Y., Tamukoh H.

ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding 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）

Image classification assumes that all classes used in testing are known. Therefore, when an unknown class data is input, it cannot be recognized correctly. A method that enables unknown classes to be identified is called open set recognition. In this paper, we propose a method of open set recognition focusing on the feature space of the classifier and a Mahalanobis-based threshold. The experimental results show that the proposed method surpasses state-of-the-art methods on some datasets, demonstrating the potential of a method focusing on the feature space.

DOI： 10.1109/ISPACS51563.2021.9650985

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124169792&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 33 ( 48 ) 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

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

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

担当区分：筆頭著者,　責任著者記述言語：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 Based Q-Learning Model for Autonomous Mobile Robots 査読有り国際誌

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

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11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）

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

Object Recognition Using Flexible Tactile Sensor 査読有り

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

Asia Pacific Conference on Robot IoT System Development and Platform 2020 2020年11月

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, Takashi Morie, Yuichi Katori

2019 International Symposium on Nonlinear Theory and Its Applications 333 - 336 2019年12月

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

小野智寛, 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, 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

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06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）

<p>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.</p>

DOI： 10.1299/jsmermd.2019.1P2-I02

その他リンク： https://ci.nii.ac.jp/naid/130007774525

Hardware implementation of brain-inspired amygdala model 査読有り国際誌

Tanaka Y., Tamukoh H.

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

担当区分：筆頭著者,　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）

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

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

Tanaka Y., Tamukoh H.

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

担当区分：筆頭著者,　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 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 査読有り国際誌

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）

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

Application of digital hardware of deep self-organizing map network 査読有り

Yuichiro Tanaka, Hakaru Tamukoh

Asia Pacific Conference on Robot IoT System Development and Platform 2018 2018年10月

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

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06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）

<p>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.</p>

DOI： 10.1299/jsmermd.2018.2P2-E02

その他リンク： https://ci.nii.ac.jp/naid/130007552042

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09月

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

We 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

Hardware-Oriented Algorithm for Deep Neural Networks Composed of Self-Organizing Maps 査読有り

Yuichiro Tanaka, Hakaru Tamukoh

Proceedings of International Workshop on Smart Info-Media Systems in Asia 116 - 121 2016年09月

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