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Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning
Analog hardware-based training provides a promising solution to developing state-of-the-art power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive random access memory (RRAM) has the potential to provide a low power alternative. The training accuracy of analog ha...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495451/ https://www.ncbi.nlm.nih.gov/pubmed/37697024 http://dx.doi.org/10.1038/s41598-023-42214-x |