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

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

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

6 Citations (Scopus)
120 Downloads (Pure)


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 publicationUnknown Host Publication
Number of pages6
ISBN (Print)978-1-5090-6763-3
Publication statusPublished online - 20 Jul 2017
EventIEEE Computer Society Annual Symposium on VLSI (ISVLSI) - Bochum
Duration: 20 Jul 2017 → …


ConferenceIEEE Computer Society Annual Symposium on VLSI (ISVLSI)
Period20/07/17 → …


  • Field programmable gate arrays
  • Hardware
  • Maintenance engineering
  • Mathematical model
  • Neurons
  • Astrocytes
  • Bio-inspired computing
  • FPGA
  • Self-repair
  • Spiking neural networks


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