1. Homeostatic Fault Tolerance in Spiking Neural Networks: A Dynamic Hardware Perspective.
- Author
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Johnson, Anju P., Liu, Junxiu, Millard, Alan G., Karim, Shvan, Tyrrell, Andy M., Harkin, Jim, Timmis, Jon, Mcdaid, Liam J., and Halliday, David M.
- Subjects
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FAULT tolerance (Engineering) , *ARTIFICIAL neural networks , *HOMEOSTASIS , *DIGITAL phase locked loops , *FIELD programmable gate arrays , *MATHEMATICAL models - 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. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
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