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SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture.

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Title: SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture.
Authors: Liu, Junxiu1, Harkin, Jim2, Maguire, Liam P.2, Mcdaid, Liam J.2, Wade, John J.2
Source: IEEE Transactions on Neural Networks & Learning Systems. Apr2018, Vol. 29 Issue 4, p1287-1300. 14p.
Subjects: Electronic systems, Biomimicry, Astrocytes, Field programmable gate arrays, Hardware, Fault-tolerant control systems
Abstract: Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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PubType: Periodical
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  Data: <searchLink fieldCode="DE" term="%22Electronic+systems%22">Electronic systems</searchLink><br /><searchLink fieldCode="DE" term="%22Biomimicry%22">Biomimicry</searchLink><br /><searchLink fieldCode="DE" term="%22Astrocytes%22">Astrocytes</searchLink><br /><searchLink fieldCode="DE" term="%22Field+programmable+gate+arrays%22">Field programmable gate arrays</searchLink><br /><searchLink fieldCode="DE" term="%22Hardware%22">Hardware</searchLink><br /><searchLink fieldCode="DE" term="%22Fault-tolerant+control+systems%22">Fault-tolerant control systems</searchLink>
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  Data: Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TNNLS.2017.2673021
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 1287
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      – SubjectFull: Electronic systems
        Type: general
      – SubjectFull: Biomimicry
        Type: general
      – SubjectFull: Astrocytes
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      – SubjectFull: Field programmable gate arrays
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      – SubjectFull: Hardware
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      – SubjectFull: Fault-tolerant control systems
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      – TitleFull: SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture.
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            NameFull: Harkin, Jim
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            NameFull: Maguire, Liam P.
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              Text: Apr2018
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              Y: 2018
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