Online learning in SNNs with e-prop and Neuromorphic Hardware

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

Abstract

Online learning in neural networks has the potential to transform AI research. By enabling new information to be assimilated into existing systems, platforms can be adaptive to unseen data and can personalise performance to an individual. A common approach in providing AI to a user is to send queries to a remote cloud service which processes the information and sends back a response. Neuromorphic hardware offers an alternate solution by providing a dedicated computing platform from which neural networks can be run locally and efficiently. This work explores the potential of the SpiNNaker neuromorphic hardware to run the eligibility propagation (e-prop) algorithm on chip whilst learning online in real time.

Original languageEnglish
Title of host publicationProceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference, NICE 2022
PublisherAssociation for Computing Machinery
Pages32-39
Number of pages8
ISBN (Electronic)9781450395595
DOIs
Publication statusPublished - 28 Mar 2022
Event2022 Annual Neuro-Inspired Computational Elements Conference, NICE 2022 - Virtual, Online, United States
Duration: 28 Mar 20221 Apr 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2022 Annual Neuro-Inspired Computational Elements Conference, NICE 2022
Country/TerritoryUnited States
CityVirtual, Online
Period28/03/221/04/22

Keywords

  • Neuromorphic Hardware
  • Online Learning
  • Spiking Neural Networks

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