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 voltagebased 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.
|Number of pages||13|
|Journal||IEEE Transactions on Biomedical Circuits and Systems|
|Early online date||28 Jul 2017|
|Publication status||Published - 1 Feb 2018|
FingerprintDive into the research topics of 'Homeostatic Fault Tolerance in Spiking Neural Networks: A Dynamic Hardware Perspective'. Together they form a unique fingerprint.
- Department of Computer Science - Senior Lecturer in Computer Science
- School of Computing and Engineering
- Centre for Planning, Autonomy and Representation of Knowledge - Member
- Centre for Thermofluids, Energy Systems and High-Performance Computing - Member
- Centre for Biomimetic Societal Futures