Optimised weight programming for analogue memory-based deep neural networks

Optimised weight programming for analogue memory-based deep neural networks

Device-level complexity represents a big shortcoming for the hardware realization of analogue memory-based deep neural networks. Mackin et al. report a generalized computational framework, translating software-trained weights into analogue hardware weights, to minimise inference accuracy degradation...

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Bibliographic Details
Main Authors: Charles Mackin, Malte J. Rasch, An Chen, Jonathan Timcheck, Robert L. Bruce, Ning Li, Pritish Narayanan, Stefano Ambrogio, Manuel Le Gallo, S. R. Nandakumar, Andrea Fasoli, Jose Luquin, Alexander Friz, Abu Sebastian, Hsinyu Tsai, Geoffrey W. Burr
Format: Article
Language:English
Published: Nature Portfolio 2022-06-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-022-31405-1

Internet

https://doi.org/10.1038/s41467-022-31405-1

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