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Algorithm for Training Neural Networks on Resistive Device Arrays

Hardware architectures composed of resistive cross-point device arrays can provide significant power and speed benefits for deep neural network training workloads using stochastic gradient descent (SGD) and backpropagation (BP) algorithm. The training accuracy on this imminent analog hardware, howev...

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書誌詳細
出版年:Front Neurosci
主要な著者: Gokmen, Tayfun, Haensch, Wilfried
フォーマット: Artigo
言語:Inglês
出版事項: Frontiers Media S.A. 2020
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC7054461/
https://ncbi.nlm.nih.gov/pubmed/32174807
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnins.2020.00103
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