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 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.
Original language | English |
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Article number | 7995041 |
Pages (from-to) | 687-699 |
Number of pages | 13 |
Journal | IEEE Transactions on Biomedical Circuits and Systems |
Volume | 65 |
Issue number | 2 |
Early online date | 28 Jul 2017 |
DOIs | |
Publication status | Published - 1 Feb 2018 |
Externally published | Yes |
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Anju Johnson
- 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
- Centre for Cybersecurity - Affiliate
- Centre for Autonomous and Intelligent Systems - Member
Person: Academic