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Deep physical neural networks trained with backpropagation
Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability(1). Deep-learning accelerators(2–9) aim to perform deep learning energy-efficiently, usually targeting the inference phase and often by exploiting...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791835/ https://www.ncbi.nlm.nih.gov/pubmed/35082422 http://dx.doi.org/10.1038/s41586-021-04223-6 |