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Accurate deep neural network inference using computational phase-change memory
In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights...
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| 出版年: | Nat Commun |
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| 主要な著者: | , , , , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Nature Publishing Group UK
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7235046/ https://ncbi.nlm.nih.gov/pubmed/32424184 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-020-16108-9 |
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