Homeostatic Fault Tolerance in Spiking Neural Networks: A Dynamic Hardware Perspective

Fault tolerance is a remarkable feature of biological systems and their self-repair capability influence modern electronic systems. In this work, 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 inter neurons, 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 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 3 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 work opens a novel way of implementing the behavior of natural fault tolerant system in hardware establishing homeostatic self-repair behavior.