Deep physical neural networks trained with backpropagation.

ABSTRACT: Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability¹. Deep-learning accelerators^2-9 aim to perform deep learning energy-efficiently, usually targeting the inference phase and often by exploiting physical substrates beyond conventional electronics. Approaches so far^10-22 have been unable to apply the backpropagation algorithm to train unconventional novel hardware in situ. The advantages of backpropagation have made it the de facto training method for large-scale neural networks, so this deficiency constitutes a major impediment. Here we introduce a hybrid in situ-in silico algorithm, called physics-aware training, that applies backpropagation to train contro

SUBMITTER: Wright LG 

PROVIDER: S-EPMC8791835 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature