Paper Abstract and Keywords |
Presentation |
2022-01-24 17:10
Ternarizing Deep Spiking Neural Network Man Wu, Yirong Kan, Van_Tinh Nguyen, Renyuan Zhang, Yasuhiko Nakashima (NAIST) VLD2021-61 CPSY2021-30 RECONF2021-69 |
Abstract |
(in Japanese) |
(See Japanese page) |
(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) |
(in English) |
deep spiking neural network / ternary weights / SNN compression / TSNN / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 343, CPSY2021-30, pp. 67-72, Jan. 2022. |
Paper # |
CPSY2021-30 |
Date of Issue |
2022-01-17 (VLD, CPSY, 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) |
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VLD2021-61 CPSY2021-30 RECONF2021-69 |
Conference Information |
Committee |
RECONF VLD CPSY IPSJ-ARC IPSJ-SLDM |
Conference Date |
2022-01-24 - 2022-01-25 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
FPGA Applications, etc. |
Paper Information |
Registration To |
CPSY |
Conference Code |
2022-01-RECONF-VLD-CPSY-ARC-SLDM |
Language |
English |
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) |
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Keyword(1) |
deep spiking neural network |
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ternary weights |
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SNN compression |
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TSNN |
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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) |
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Speaker |
Author-1 |
Date Time |
2022-01-24 17:10:00 |
Presentation Time |
25 minutes |
Registration for |
CPSY |
Paper # |
VLD2021-61, CPSY2021-30, RECONF2021-69 |
Volume (vol) |
vol.121 |
Number (no) |
no.342(VLD), no.343(CPSY), no.344(RECONF) |
Page |
pp.67-72 |
#Pages |
6 |
Date of Issue |
2022-01-17 (VLD, CPSY, RECONF) |
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