Implementing learning on the SpiNNaker universal neural chip multiprocessor

Xin Jin, Alexander Rast, Francesco Galluppi, M. Mukaram Khan, Stephen Furber

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

Abstract

Large-scale neural simulation requires high-performance hardware with on-chip learning. Using SpiNNaker, a universal neural network chip multiprocessor, we demonstrate an STDP implementation as an example of programmable on-chip learning for dedicated neural hardware. Using a scheme driven entirely by pre-synaptic spike events, we optimize both the data representation and processing for efficiency of implementation. The deferred-event model provides a reconfigurable timing record length to meet different accuracy requirements. Results demonstrate successful STDP within a multi-chip simulation containing 60 neurons and 240 synapses. This optimisable learning model illustrates the scalable general-purpose techniques essential for developing functional learning rules on general-purpose, parallel neural hardware.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages425-432
Number of pages8
Volume5863 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2009
Event16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand
Duration: 1 Dec 20095 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5863 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference16th International Conference on Neural Information Processing, ICONIP 2009
Country/TerritoryThailand
CityBangkok
Period1/12/095/12/09

Keywords

  • Event-Driven
  • Learning
  • Neural
  • Spiking
  • SpiNNaker
  • STDP

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