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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
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Download PDF 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)  
Keyword(1) deep spiking neural network  
Keyword(2) ternary weights  
Keyword(3) SNN compression  
Keyword(4) 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
Date of Issue 2022-01-17 (VLD, CPSY, RECONF) 


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