NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Volta...
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains problematically high. An effective strategy for reducing such consumption is supply-voltage reduction, but if done too aggressively, it can lead to accuracy degradation. This is due to random bit-flips in static random access memory (SRAM),...
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NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
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TN_cdi_proquest_journals_2831656315
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2831656315
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2331-8422
