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Paper Abstract and Keywords
Presentation 2020-01-24 16:10
26-bit 400-neuron 0.3-ksps FORCE Learning FPGA Core for Reservoir Computing
koyo Minamikawa, Shunya Suzuki, Megumi Akai-Kasaya, Tetsuya asai (Hokkaido Univ.) NLP2019-98
Abstract (in Japanese) (See Japanese page) 
(in English) Reservoir Computing (RC) is a type of Recurrent Neural Network (RNN) and are used for processing time series.
Since the learning part is only the weight of the output layer and the amount of calculation is smaller than that of the RNN, the reservoir computing is expected to learn with low power.
Inverse matrix is required to learn in an RC, and numerous resources are required to implement the hardware.
However, by using FORCE learning [1], calculations can be performed only with matrix operation without using inverse matrix operation.
In this study, we implement a dedicated architecture for learning reservoir computing that drives with low power consumption using FORCE learning with FPGA.
We designed the architecture to operate on the cheapest possible FPGA.
The board was actually created and evaluated from the viewpoint of accuracy and power consumption.
Keyword (in Japanese) (See Japanese page) 
(in English) Neural Network / Reservoir Computing / FPGA / Power saving / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 381, NLP2019-98, pp. 67-72, Jan. 2020.
Paper # NLP2019-98 
Date of Issue 2020-01-16 (NLP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee NLP NC  
Conference Date 2020-01-23 - 2020-01-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Miyakojima Marine Terminal 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2020-01-NLP-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) 26-bit 400-neuron 0.3-ksps FORCE Learning FPGA Core for Reservoir Computing 
Sub Title (in English)  
Keyword(1) Neural Network  
Keyword(2) Reservoir Computing  
Keyword(3) FPGA  
Keyword(4) Power saving  
1st Author's Name koyo Minamikawa  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Shunya Suzuki  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Megumi Akai-Kasaya  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Tetsuya asai  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2020-01-24 16:10:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # NLP2019-98 
Volume (vol) vol.119 
Number (no) no.381 
Page pp.67-72 
Date of Issue 2020-01-16 (NLP) 

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