Homeostatic Fault Tolerance in Spiking Neural Networks: A Dynamic Hardware Perspective
  
  
    
    Johnson, Anju P, Junxiu, Liu, Millard, Alan G, Shvan, Karim, Tyrrel, Andy M, Harkin, Jim, Timmis, Jonathan, McDaid, Liam J and Halliday, David M
  
(2017)
Homeostatic Fault Tolerance in Spiking Neural Networks: A Dynamic Hardware Perspective.
    IEEE Transactions on Circuits and Systems, 65 (2).
     pp. 687-689.
     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.
      
     
    
      
      
      
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