Impact of Synaptic Device Variations on Classification Accuracy in a Binarized Neural Network
Impact of Synaptic Device Variations on Classification Accuracy in a Binarized Neural Network
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Brain-inspired neuromorphic systems (hardware neural networks) are expected to be an energy-efficient computing architecture for solving cognitive tasks, which critically depend on the development of reliable synaptic weight storage (
i.e
., synaptic device). Although various nanoelectronic devices have successfully reproduced the learning ru...
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Impact of Synaptic Device Variations on Classification Accuracy in a Binarized Neural Network
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TN_cdi_doaj_primary_oai_doaj_org_article_7528dffafed64e5f861963b6b1186d67
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7528dffafed64e5f861963b6b1186d67
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2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-019-51814-5
