On-Chip Learning with a 15-neuron Digital Oscillatory Neural Network Implemented on ZYNQ Processor

Madeleine Abernot, Thierry Gil, Aida Todri-Sanial

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

3 Citations (Scopus)

Abstract

Real-time on-chip learning is an important feature for current neuromorphic computing to enable smart embedded systems capable of learning. Neuromorphic computing based on Oscillatory Neural Networks (ONNs) are networks of coupled oscillators computing with phase information. ONNs with fully-connected connections can perform auto-associative memory applications when trained with unsupervised learning rules. In this paper, we propose for the first time an architecture to perform on-chip learning with a digitally implemented ONN. We implement the digital ONN with programmable logic of a ZYNQ processor and we perform learning on the processing system of the same chip. We validate our solution on a 15-neuron ONN trained with either Hebbian or Storkey learning rules up to three patterns. We report a stable resource utilization for both learning rules and timing from 119 μs (Hebbian) to 163 μs (Storkey). Additionally, accuracy is equal to the off-chip learning implementation.

Original languageEnglish
Title of host publicationICONS 2022 - Proceedings of International Conference on Neuromorphic Systems 2022
PublisherAssociation for Computing Machinery, Inc
Pages29:1-29:4
Number of pages4
ISBN (Electronic)978-1-4503-9789-6
DOIs
Publication statusPublished - 7 Sept 2022
Externally publishedYes
Event2022 International Conference on Neuromorphic Systems, ICONS 2022 - Knoxville, United States
Duration: 27 Jul 202229 Jul 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2022 International Conference on Neuromorphic Systems, ICONS 2022
Country/TerritoryUnited States
CityKnoxville
Period27/07/2229/07/22

Keywords

  • Auto-associative Memory
  • On-chip Learning
  • Oscillatory Neural Networks
  • Pattern Recognition

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