Device Variation Effects on Neural Network Inference Accuracy in Analog In‐Memory Computing Systems
Device Variation Effects on Neural Network Inference Accuracy in Analog In‐Memory Computing Systems
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Weinheim: John Wiley & Sons, Inc
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English
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Weinheim: John Wiley & Sons, Inc
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In analog in‐memory computing systems based on nonvolatile memories such as resistive random‐access memory (RRAM), neural network models are often trained offline and then the weights are programmed onto memory devices as conductance values. The programmed weight values inevitably deviate from the target values during the programming process. This...
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Device Variation Effects on Neural Network Inference Accuracy in Analog In‐Memory Computing Systems
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TN_cdi_doaj_primary_oai_doaj_org_article_350167ba1f1d481ab3ce6c5ff2adea55
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_350167ba1f1d481ab3ce6c5ff2adea55
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ISSN
2640-4567
E-ISSN
2640-4567
DOI
10.1002/aisy.202100199
