Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks

Shvan Karim, Jim Harkin, Liam McDaid, Bryan Gardiner, Junxiu Liu, David M. Halliday, Andy M. Tyrrell, Jon Timmis, Alan Millard, Anju Johnson

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

12 Citations (SciVal)

Abstract

This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
EditorsRicardo Reis, Mircea Stan, Michael Huebner, Nikolaos Voros
PublisherIEEE Press
Pages421-426
Number of pages6
ISBN (Electronic)9781509067626
DOIs
Publication statusPublished - 20 Jul 2017
Event2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017 - Bochum, North Rhine-Westfalia, Germany
Duration: 03 Jul 201705 Jul 2017

Publication series

NameProceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
Volume2017-July
ISSN (Print)2159-3469
ISSN (Electronic)2159-3477

Conference

Conference2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
Country/TerritoryGermany
CityBochum, North Rhine-Westfalia
Period03 Jul 201705 Jul 2017

Keywords

  • Astrocytes
  • Bio-inspired computing
  • FPGA
  • Self-repair
  • Spiking neural networks

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