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| Paper Abstract and Keywords | ||
| Presentation | 2022-09-07 14:10 Efficient Learning of Spiking Neural Networks with Genetic Algorithm and its FPGA Acceleration Taiki Watanabe, Yukinori Sato (TUT)  RECONF2022-26 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Spiking Neural Network (SNN) is one of the promising models of neuromorphic architecture. A learning method using a genetic algorithm is proposed for this SNN. This method enables optimization by adding and removing nodes and edges in the network, in addition to adjusting the weight parameters. In this paper, we propose parallelization, partial C implementation, and hardware acceleration methods for accelerating the training of SNNs with genetic algorithms. We also implement an FPGA using high-level synthesis based on the C language implementation. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | / / / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 174, RECONF2022-26, pp. 1-6, Sept. 2022. | |
| Paper # | RECONF2022-26 | |
| Date of Issue | 2022-08-31 (RECONF) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
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) | |
| Notes on Review | This article is a technical report without peer review, and its polished version will be published elsewhere. | |
| Download PDF | RECONF2022-26 |
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| Conference Information | |
| Committee | RECONF |
| Conference Date | 2022-09-07 - 2022-09-08 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | emCAMPUS STUDIO |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Reconfigurable system, etc. |
| Paper Information | |
| Registration To | RECONF |
| Conference Code | 2022-09-RECONF |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Efficient Learning of Spiking Neural Networks with Genetic Algorithm and its FPGA Acceleration |
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| 1st Author's Name | Taiki Watanabe |
| 1st Author's Affiliation | Toyohashi University of Technology (TUT) |
| 2nd Author's Name | Yukinori Sato |
| 2nd Author's Affiliation | Toyohashi University of Technology (TUT) |
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| Speaker | Author-1 |
| Date Time | 2022-09-07 14:10:00 |
| Presentation Time | 25 minutes |
| Registration for | RECONF |
| Paper # | RECONF2022-26 |
| Volume (vol) | vol.122 |
| Number (no) | no.174 |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2022-08-31 (RECONF) |
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