| Presentation | 2022-09-07 Efficient Learning of Spiking Neural Networks with Genetic Algorithm and its FPGA Acceleration Taiki Watanabe, Yukinori Sato, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
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| Paper # | RECONF2022-26 |
| Date of Issue | 2022-08-31 (RECONF) |
| Conference Information | |
| Committee | RECONF |
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| Conference Date | 2022/9/7(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | emCAMPUS STUDIO |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Reconfigurable system, etc. |
| Chair | Kentaro Sano(RIKEN) |
| Vice Chair | Yoshiki Yamaguchi(Tsukuba Univ.) / Tomonori Izumi(Ritsumeikan Univ.) |
| Secretary | Yoshiki Yamaguchi(NEC) / Tomonori Izumi(Toyohashi Univ. of Tech.) |
| Assistant | Yukitaka Takemura(INTEL) / Yasunori Osana(Ryukyu Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Reconfigurable Systems |
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| Language | JPN |
| 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 |
| Sub Title (in English) | |
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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) |
| Date | 2022-09-07 |
| Paper # | RECONF2022-26 |
| Volume (vol) | vol.122 |
| Number (no) | RECONF-174 |
| Page | pp.pp.1-6(RECONF), |
| #Pages | 6 |
| Date of Issue | 2022-08-31 (RECONF) |