Backpropagation through nonlinear units for the all-optical training of neural networksстатья
Статья опубликована в высокорейтинговом журнале
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Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 12 января 2022 г.
Авторы:
Xianxin Guo,
Barrett Thomas D.,
Wang Zhiming M.,
Lvovsky A.I.
Аннотация:We propose a practical scheme for end-to-end optical backpropagation in neural networks. Using saturable absorption for the nonlinear units, we find that the backward-propagating gradients required to train the network can be approximated in a surprisingly simple pump-probe scheme that requires only simple passive optical elements. Simulations show that, with readily obtainable optical depths, our approach can achieve equivalent performance to state-of-the-art computational networks on image classification benchmarks, even in deep networks with multiple sequential gradient approximation. With backpropagation through nonlinear units being an outstanding challenge to the field, this work provides a feasible path toward truly all-optical neural networks.