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SPANNER: A Self-Repairing Spiking NeuralNetwork Hardware Architecture

Liu, Junxiu, Harkin, J, Maguire, LP, McDaid, LJ and Wade, John (2017) SPANNER: A Self-Repairing Spiking NeuralNetwork Hardware Architecture. IEEE Transactions on Neural Networks and Learning Systems, X . pp. 1-12. [Journal article]

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DOI: 10.1109/TNNLS.2017.2673021

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%.

Item Type:Journal article
Keywords:fault tolerant computing;neural nets;SPANNER;astrocyte cells;astrocyte-neuron networks;fault tolerance techniques;fine-grained repair capability;self-detect faults;
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:37159
Deposited By: Dr Jim Harkin
Deposited On:13 Mar 2017 09:26
Last Modified:13 Mar 2017 09:26

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