A time-to-first-spike coding and conversion aware training for energy-efficient deep spiking neural network processor design

Dongwoo Lew, Kyungchul Lee, Jongsun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In this paper, we present an energy-efficient SNN architecture, which can seamlessly run deep spiking neural networks (SNNs) with improved accuracy. First, we propose a conversion aware training (CAT) to reduce ANN-to-SNN conversion loss without hardware implementation overhead. In the proposed CAT, the activation function developed for simulating SNN during ANN training, is efficiently exploited to reduce the data representation error after conversion. Based on the CAT technique, we also present a time-to-first-spike coding that allows lightweight logarithmic computation by utilizing spike time information. The SNN processor design that supports the proposed techniques has been implemented using 28nm CMOS process. The processor achieves the top-1 accuracies of 91.7%, 67.9% and 57.4% with inference energy of 486.7uJ, 503.6uJ, and 1426uJ to process CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively, when running VGG-16 with 5bit logarithmic weights.

Original languageEnglish
Title of host publicationProceedings of the 59th ACM/IEEE Design Automation Conference, DAC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages265-270
Number of pages6
ISBN (Electronic)9781450391429
DOIs
Publication statusPublished - 2022 Jul 10
Event59th ACM/IEEE Design Automation Conference, DAC 2022 - San Francisco, United States
Duration: 2022 Jul 102022 Jul 14

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference59th ACM/IEEE Design Automation Conference, DAC 2022
Country/TerritoryUnited States
CitySan Francisco
Period22/7/1022/7/14

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS This work was supported by the National Research Foundation of Korea grant funded by the Korea government (No. NRF-2020R1A2C3014820).

Publisher Copyright:
© 2022 ACM.

Keywords

  • ANN-to-SNN conversion
  • logarithmic computations
  • spiking neural network
  • temporal coding

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modelling and Simulation

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