In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memor...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Yiyang, Xiao, T. Patrick, Bennett, Christopher H., Isele, Erik, Melianas, Armantas, Tao, Hanbo, Marinella, Matthew J., Salleo, Alberto, Fuller, Elliot J., Talin, A. Alec
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060477/
https://www.ncbi.nlm.nih.gov/pubmed/33897351
http://dx.doi.org/10.3389/fnins.2021.636127

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060477/
https://www.ncbi.nlm.nih.gov/pubmed/33897351
http://dx.doi.org/10.3389/fnins.2021.636127

Ejemplares similares