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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Detalles Bibliográficos
Autores principales: Joshi, Vinay, Le Gallo, Manuel, Haefeli, Simon, Boybat, Irem, Nandakumar, S. R., Piveteau, Christophe, Dazzi, Martino, Rajendran, Bipin, Sebastian, Abu, Eleftheriou, Evangelos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7235046/
https://www.ncbi.nlm.nih.gov/pubmed/32424184
http://dx.doi.org/10.1038/s41467-020-16108-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7235046/
https://www.ncbi.nlm.nih.gov/pubmed/32424184
http://dx.doi.org/10.1038/s41467-020-16108-9

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