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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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| 出版年: | Front Neurosci |
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| 主要な著者: | , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Frontiers Media S.A.
2020
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| 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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