| Presentation | 2022-01-24 Ternarizing Deep Spiking Neural Network Man Wu, Yirong Kan, Van_Tinh Nguyen, Renyuan Zhang, Yasuhiko Nakashima, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The feasibility of ternarizing spiking neural networks (SNNs) is studied in this work toward trading a slight accuracy for significantly reducing computational and memory costs. By leveraging a parametric integrate-and-fire (PIF) neuron with learnable threshold and spike-timing-dependent backpropagation (STDB) learning rule, the ternarized spiking neural networks (TSNNs) enable directly trained with low latency and negligible loss of accuracy. To this end, a paradigm for binary-ternary dotproduct operation is realized during the inference; therefore, the TSNNs achieve up to 16x model compression in contrast to the full precision SNNs. Moreover, to mitigate the accuracy gap, an optimized TSNN with a spiking ResNet structure is introduced into TSNN. For proof-of-concept, we evaluate the prototype of proposed TSNN on N-MNIST, CIFAR-10, CIFAR-100, which achieve 98.43%, 89.07%, 65.24% accuracy with 4 timesteps, respectively. On the basis of this prototype, the optimized TSNN improves by 0.84% and 0.51% over CIFAR-10 and CIFAR-100 datasets, respectively. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | deep spiking neural network / ternary weights / SNN compression / TSNN |
| Paper # | VLD2021-61,CPSY2021-30,RECONF2021-69 |
| Date of Issue | 2022-01-17 (VLD, CPSY, RECONF) |
| Conference Information | |
| Committee | RECONF / VLD / CPSY / IPSJ-ARC / IPSJ-SLDM |
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| Conference Date | 2022/1/24(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | FPGA Applications, etc. |
| Chair | Kentaro Sano(RIKEN) / Kazutoshi Kobayashi(Kyoto Inst. of Tech.) / Michihiro Koibuchi(NII) / Hiroshi Inoue(Kyushu Univ.) / Yuichi Nakamura(NEC) |
| Vice Chair | Yoshiki Yamaguchi(Tsukuba Univ.) / Tomonori Izumi(Ritsumeikan Univ.) / Minako Ikeda(NTT) / Kota Nakajima(Fujitsu Lab.) / Tomoaki Tsumura(Nagoya Inst. of Tech.) |
| Secretary | Yoshiki Yamaguchi(NEC) / Tomonori Izumi(Tokyo Inst. of Tech.) / Minako Ikeda(Osaka Univ.) / Kota Nakajima(NEC) / Tomoaki Tsumura(JAIST) / (Hitachi) / (Univ. of Tokyo) |
| Assistant | Yukitaka Takemura(INTEL) / Yasunori Osana(Ryukyu Univ.) / / Ryohei Kobayashi(Tsukuba Univ.) / Takaaki Miyajima(Meiji Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Reconfigurable Systems / Technical Committee on VLSI Design Technologies / Technical Committee on Computer Systems / Special Interest Group on System Architecture / Special Interest Group on System and LSI Design Methodology |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Ternarizing Deep Spiking Neural Network |
| Sub Title (in English) | |
| Keyword(1) | deep spiking neural network |
| Keyword(2) | ternary weights |
| Keyword(3) | SNN compression |
| Keyword(4) | TSNN |
| 1st Author's Name | Man Wu |
| 1st Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| 2nd Author's Name | Yirong Kan |
| 2nd Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| 3rd Author's Name | Van_Tinh Nguyen |
| 3rd Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| 4th Author's Name | Renyuan Zhang |
| 4th Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| 5th Author's Name | Yasuhiko Nakashima |
| 5th Author's Affiliation | Nara Institute of Science and Technology(NAIST) |
| Date | 2022-01-24 |
| Paper # | VLD2021-61,CPSY2021-30,RECONF2021-69 |
| Volume (vol) | vol.121 |
| Number (no) | VLD-342,CPSY-343,RECONF-344 |
| Page | pp.pp.67-72(VLD), pp.67-72(CPSY), pp.67-72(RECONF), |
| #Pages | 6 |
| Date of Issue | 2022-01-17 (VLD, CPSY, RECONF) |