Abstract
In this paper, we present spiking neural network (SNN) conversion technique optimized for converting low bit-width artificial neural networks (ANN) trained with quantization aware training (QAT). Conventional conversion technique suffers significant accuracy drop on QAT ANNs due to different activation function used for QAT ANNs. To minimize such accuracy drop of the conventional conversion, the proposed technique uses Spike-Norm Skip, which selectively applies threshold balancing. In addition, subtraction based reset is used to further reduce accuracy degradation. The proposed conversion technique achieves an accuracy of 89.92% (0.68% drop) with a 5-bit weight on CIFAR-10 using VGG-16.
Original language | English |
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Title of host publication | 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665435536 |
DOIs | |
Publication status | Published - 2021 Jun 27 |
Event | 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021 - Jeju, Korea, Republic of Duration: 2021 Jun 27 → 2021 Jun 30 |
Publication series
Name | 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021 |
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Conference
Conference | 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021 |
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Country/Territory | Korea, Republic of |
City | Jeju |
Period | 21/6/27 → 21/6/30 |
Bibliographical note
Funding Information:This work was supported in part by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2018-0-01433) supervised by the IITP(Institute for Information & communications Technology Promotion), and in part by the Industrial Strategic Technology Development Program(10077445, Development of SoC technology based on Spiking Neural Cell for smart mobile and IoT Devices) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea)
Publisher Copyright:
© 2021 IEEE.
Keywords
- ANN-SNN conversion
- quantization aware training
- spiking neural networks
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Information Systems
- Electrical and Electronic Engineering