Optimised weight programming for analogue memory-based deep neural networks

Analogue memory-based deep neural networks provide energy-efficiency and per-area throughput gains relative to state-of-the-art digital counterparts such as graphics processing units. Recent advances focus largely on hardware-aware algorithmic training and improvements to circuits, architectures, an...

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Detalles Bibliográficos
Autores principales: Mackin, Charles, Rasch, Malte J., Chen, An, Timcheck, Jonathan, Bruce, Robert L., Li, Ning, Narayanan, Pritish, Ambrogio, Stefano, Le Gallo, Manuel, Nandakumar, S. R., Fasoli, Andrea, Luquin, Jose, Friz, Alexander, Sebastian, Abu, Tsai, Hsinyu, Burr, Geoffrey W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247051/
https://www.ncbi.nlm.nih.gov/pubmed/35773285
http://dx.doi.org/10.1038/s41467-022-31405-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247051/
https://www.ncbi.nlm.nih.gov/pubmed/35773285
http://dx.doi.org/10.1038/s41467-022-31405-1

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