FPGA-based Fault-injection and Data Acquisition of Self-repairing Spiking Neural Network Hardware

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

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

6 Citations (Scopus)

Abstract

Spiking Astrocyte-neuron Networks (SANNs) model the adaptive/repair feature of the human brain. They integrate astrocyte cells with spiking neurons to facilitate a distributed and fine-grained self-repair capability at the synapse level. SANNs are more complex with the addition of astrocyte cells and require longer simulation times, as they are dynamic over much longer time-scales than traditional neural networks. Therefore, dedicated FPGA accelerators offer reductions in simulation times. To support the acceleration of SANNs, the capability of fault injection to synapses and monitoring significant levels of neuron and astrocyte data for off-chip transmission to PC-based analysis, are required. This paper presents an FPGA-based monitoring platform (FMP) for injecting faults and capturing and analyzing data acquired from the SANN FPGA accelerator, Astrobyte. The FMP uses custom logic and a NIOS II based system to control fault injection and data monitoring on the FPGA. Results show accurate accelerated simulations of fault injection scenarios using FMP with speedups up to 65 times greater compared with equivalent Matlab implementations.

Original languageEnglish
Title of host publication2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PublisherIEEE Press
ISBN (Electronic)9781538648810
DOIs
Publication statusPublished - 26 Apr 2018
Event2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
Duration: 27 May 201830 May 2018

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2018-May
ISSN (Print)0271-4310

Conference

Conference2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Country/TerritoryItaly
CityFlorence
Period27 May 201830 May 2018

Keywords

  • Astrocytes
  • Data Acquisition
  • Fault injection
  • FPGA acceleration
  • Self repair
  • Spiking neural network

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