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 contribution

4 Citations (Scopus)

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 publication 2017 IEEE Computer Society Annual Symposium on VLSI
EditorsMichael Hübner, Ricardo Reis, Mircea Stan, Nikolaos Voros
PublisherIEEE
Pages421-426
Number of pages6
ISBN (Electronic)9781509067626
ISBN (Print)9781509067633
DOIs
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
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7985547 (Link to Conference Proceedings)

Conference

ConferenceIEEE Computer Society Annual Symposium on Very-Large-Scale-Integration
Abbreviated titleVLSI 2017
CountryGermany
CityBochum
Period3/07/175/07/17
Internet address

Fingerprint Dive into the research topics of 'Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks'. Together they form a unique fingerprint.

  • Cite this

    Karim, S., Harkin, J., McDaid, L., Gardiner, B., Liu, J., Halliday, D., ... Johnson, A. (2017). Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. In M. Hübner, R. Reis, M. Stan, & N. Voros (Eds.), 2017 IEEE Computer Society Annual Symposium on VLSI (pp. 421-426). IEEE. https://doi.org/10.1109/ISVLSI.2017.80