SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture

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Abstract

Recent research has shown that a glial cell ofastrocyte underpins a self-repair mechanism in the human brainwhere spiking neurons provide direct and indirect feedbacks topre-synaptic terminals. These feedbacks modulate the synaptictransmission probability of release (PR). When synaptic faultsoccur the neuron becomes silent or near silent due to the low PR ofsynapses; whereby the PRs of remaining healthy synapses arethen increased by the indirect feedback from the astrocyte cell. Inthis paper, a novel hardware architecture of Self-rePAiringspiking Neural NEtwoRk (SPANNER) is proposed, which mimicsthis self-repairing capability in the human brain. This paperdemonstrates that the hardware can self-detect and self-repairsynaptic faults without the conventional components for the faultdetection and fault repairing. Experimental results show thatSPANNER can maintain the system performance with faultdensities of up to 40%, and more importantly SPANNER has onlya 20% performance degradation when the self-repairingarchitecture is significantly damaged at a fault density of 80%.
Original languageEnglish
Pages (from-to)1287-1300
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume29
Issue number4
Early online date6 Mar 2017
DOIs
Publication statusPublished - 15 Mar 2018

Keywords

  • fault tolerant computing
  • neural nets
  • SPANNER
  • astrocyte cells
  • astrocyte-neuron networks
  • fault tolerance techniques
  • fine-grained repair capability
  • self-detect faults

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