FPGA-Based Fault-Injection and Data Acquisition of Self-Repairing Spiking Neural Network Hardware

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

Research output: Contribution to conferencePaperpeer-review

6 Citations (Scopus)
162 Downloads (Pure)


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
Number of pages5
Publication statusPublished (in print/issue) - 27 May 2018
EventIEEE International Symposium on Circuits and Systems - Florence, Italy
Duration: 27 May 201830 May 2018


ConferenceIEEE International Symposium on Circuits and Systems
Abbreviated titleISCAS


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


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