Early Image Termination Technique during STDP Training of Spiking Neural Network

Dongwoo Lew, Jongsun Park

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

Abstract

Spiking Neural Network (SNN) is a breed of neural networks that seek to achieve low energy and power by more closely mimicking biological brains. SNNs are often trained using lightweight unsupervised learning such as Spike Time Dependent Plasticity (STDP). However, STDP is prone to redundant time steps during training since STDP cannot determine current image needs further training or not. To reduce redundant time steps and lower energy costs during STDP training, we propose a novel technique that terminates training upon an image preemptively. The proposed technique reduces time steps by 44% with accuracy drop of 0.91% on MNIST.

Original languageEnglish
Title of host publicationProceedings - International SoC Design Conference, ISOCC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages79-80
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
Country/TerritoryKorea, Republic of
CityYeosu
Period20/10/2120/10/24

Keywords

  • Spike Timing Dependant Plasticity (STDP)
  • Spking 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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