Spiking Neural Networks for digital hand-written number recognition

Dian Sheng, Rongxuan Xu, Qinan Wang, Chun Zhao*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Nowadays, the advancement of deep learning has been staggering during the past decades. The state-of-art spiking neural networks (SNNs) demonstrate outstanding characteristics in accuracy, power-efficiency, and spiking timing-dependent plasticity (STDP). In view of these advantages, SNNs are a promising candidate for neural morphic application. This paper presents the performances and applications of SNNs, which are used to recognize digital hand-written numbers.

Original languageEnglish
Title of host publicationProceedings - International SoC Design Conference 2022, ISOCC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages185-186
Number of pages2
ISBN (Electronic)9781665459716
DOIs
Publication statusPublished - 2022
Event19th International System-on-Chip Design Conference, ISOCC 2022 - Gangneung-si, Korea, Republic of
Duration: 19 Oct 202222 Oct 2022

Publication series

NameProceedings - International SoC Design Conference 2022, ISOCC 2022

Conference

Conference19th International System-on-Chip Design Conference, ISOCC 2022
Country/TerritoryKorea, Republic of
CityGangneung-si
Period19/10/2222/10/22

Keywords

  • Leaky integrate-and-fire (LIF)
  • MINIST
  • Neural computing

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