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

Karim, S., Harkin, J., McDaid, L., Gardiner, B., Liu, J., Halliday, D.M., Tyrrell, A.M., Timmis, Jonathan, Millard, A. and Johnson, A. (2017) Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks. IEEE Computer Society Annual Symposium on VLSI, ISVLSI, 2017-J. pp. 421-426. ISSN 2159-3477

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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.

Item Type: Article
Subjects: Computing > Artificial Intelligence
Divisions: Faculty of Technology > School of Computer Science
Depositing User: Jonathan Timmis
Date Deposited: 27 Feb 2020 14:32
Last Modified: 27 Feb 2020 14:32

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