Inference and Gradient Measurement for Backpropagation in Photonic Neural Networks

Authors:

1 January 2022

conference paper
Published by Optica Publishing Group

p. STh5G.2
DOI:10.1364/cleo_si.2022.sth5g.2

Abstract

We experimentally demonstrate in situ backpropagation in a programmable nanophotonic interferometer network, achieving inference accuracies matching digital implementations. Error gradients are computed by simultaneously measuring optical interference at intermediate network components, eliminating expensive digital computations.

Keywords

INTERFERENCE
LIGHT PROPAGATION
NEURAL NETWORKS
OPTICAL SIGNALS
PHASE MEASUREMENT
PHASE SHIFT

This publication has 2 references indexed in Scilit:

Training of photonic neural networks through in situ backpropagation and gradient measurement
Optica, 2018
Deep learning with coherent nanophotonic circuits
Nature Photonics, 2017