Early Termination of STDP Learning with Spike Counts in Spiking Neural Networks

Sunghyun Choi, Jongsun Park

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

Abstract

Spiking neural network (SNN) is considered as one of the most promising candidates for designing neuromorphic hardware due to its low power computing capability. Since SNNs are made from imitating features of the human brain, bio-plausible spike-Timing-dependent plasticity (STDP) learning rule can be adjusted to perform unsupervised learning of SNN. In this paper, we present a spike count based early termination technique for STDP learning in SNN. To reduce redundant timesteps and calculations, spike counts of output neurons can be used to terminate the training process beforehand, thus latency and energy can be decreased. The proposed scheme reduces 50.7% of timesteps and 51.1% of total weight update during training with 0.35% accuracy drop in MNIST application.

Original languageEnglish
Title of host publicationProceedings - International SoC Design Conference, ISOCC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages75-76
Number of pages2
ISBN (Electronic)9781728183312
DOIs
Publication statusPublished - 2020 Oct 21
Event17th International System-on-Chip Design Conference, ISOCC 2020 - Yeosu, Korea, Republic of
Duration: 2020 Oct 212020 Oct 24

Publication series

NameProceedings - International SoC Design Conference, ISOCC 2020

Conference

Conference17th International System-on-Chip Design Conference, ISOCC 2020
CountryKorea, Republic of
CityYeosu
Period20/10/2120/10/24

Keywords

  • image classification
  • spike-Timing-dependent plasticity(STDP)
  • Spiking neural network(SNN)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Instrumentation
  • Artificial Intelligence
  • Hardware and Architecture

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