Johnson, Anju Pulikkakudi orcid.org/0000-0002-7017-1644, Liu, Junxiu, Millard, Alan Gregory orcid.org/0000-0002-4424-5953 et al. (6 more authors) (2018) Homeostatic Fault Tolerance in Spiking Neural Networks:A Dynamic Hardware Perspective. Ieee transactions on circuits and systems i-Regular papers. 7995041. pp. 687-699. ISSN 1549-8328
Abstract
Fault tolerance is a remarkable feature of biological systems and their self-repair capability influence modern electronic systems. In this paper, we propose a novel plastic neural network model, which establishes homeostasis in a spiking neural network. Combined with this plasticity and the inspiration from inhibitory interneurons, we develop a fault-resilient robotic controller implemented on an FPGA establishing obstacle avoidance task. We demonstrate the proposed methodology on a spiking neural network implemented on Xilinx Artix-7 FPGA. The system is able to maintain stable firing (tolerance ±10%) with a loss of up to 75% of the original synaptic inputs to a neuron. Our repair mechanism has minimal hardware overhead with a tuning circuit (repair unit) which consumes only three slices/neuron for implementing a threshold voltage-based homeostatic fault-tolerant unit. The overall architecture has a minimal impact on power consumption and, therefore, supports scalable implementations. This paper opens a novel way of implementing the behavior of natural fault tolerant system in hardware establishing homeostatic self-repair behavior.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | FPGA,Self-repair,bio-inspired engineering,dynamic partial reconfiguration,fault tolerance,homeostasis,mixed-mode clock manager,phase locked loop |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number EPSRC EP/N007050/1 |
Depositing User: | Pure (York) |
Date Deposited: | 17 Aug 2017 13:15 |
Last Modified: | 21 Jan 2025 17:28 |
Published Version: | https://doi.org/10.1109/TCSI.2017.2726763 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/TCSI.2017.2726763 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:120293 |
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Filename: 07995041.pdf
Description: Homeostatic Fault Tolerance in Spiking Neural Networks: A Dynamic Hardware Perspective
Licence: CC-BY 2.5