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

Shvan Karim, Jim Harkin, Liam 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

12 Citations (Scopus)


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 publication 2017 IEEE Computer Society Annual Symposium on VLSI
EditorsMichael Hübner, Ricardo Reis, Mircea Stan, Nikolaos Voros
Number of pages6
ISBN (Electronic)9781509067626
ISBN (Print)9781509067633
Publication statusPublished - 24 Jul 2017
Externally publishedYes
EventIEEE Computer Society Annual Symposium on Very-Large-Scale-Integration - Bochum, Germany
Duration: 3 Jul 20175 Jul 2017 (Link to Conference Proceedings)


ConferenceIEEE Computer Society Annual Symposium on Very-Large-Scale-Integration
Abbreviated titleVLSI 2017
Internet address


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