SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture

Junxiu Liu; J Harkin; LP Maguire; LJ McDaid; John Wade

doi:10.1109/TNNLS.2017.2673021

SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture

Junxiu Liu, J Harkin, LP Maguire, LJ McDaid, John Wade

Research output: Contribution to journal › Article › peer-review

49 Citations (Scopus)

372 Downloads (Pure)

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 language	English
Pages (from-to)	1287-1300
Number of pages	14
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	29
Issue number	4
Early online date	6 Mar 2017
DOIs	https://doi.org/10.1109/TNNLS.2017.2673021
Publication status	Published (in print/issue) - 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TNNLS.2017.2673021

Accepted version 20 2 2017Author Accepted Manuscript, 583 KB

https://pure.ulster.ac.uk/en/publications/spanner-a-self-repairing-spiking-neuralnetwork-hardware-architect-3

Cite this

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title = "SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture",

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%.",

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author = "Junxiu Liu and J Harkin and LP Maguire and LJ McDaid and John Wade",

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AU - Wade, John

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KW - astrocyte-neuron networks

KW - fault tolerance techniques

KW - fine-grained repair capability

KW - self-detect faults

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SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture

Abstract

Keywords

UN SDGs

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