Experimentally realized in situ backpropagation for deep learning in photonic neural networks

By Sunil Pai, Zhanghao Sun, Tyler W. Hughes, Taewon Park, Ben Bartlett, Ian A. D. Williamson, Momchil Minkov, M. Milanizadeh, Nathnael S. Abebe, Francesco Morichetti and 4 more. Science, 2022

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Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using “in situ backpropagation,” a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations (>94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning. Description Editor’s summary Commercial applications of machine learning (ML) are associated with exponentially increasing energy costs, requiring the development of energy-efficient analog alternatives. Many conventional ML methods use digital backpropagation for neural network training, which is a computationally expensive task. Pai et al. designed a photonic neural network chip to allow efficient and feasible in situ backpropagation training by monitoring optical power passing either forward or backward through each waveguide segment of the chip (see the Perspective by Roques-Carmes). The presented proof-of-principle experimental realization of on-chip backpropagation training demonstrates one of the ways that ML could fundamentally change in the future, with most of the computation taking place optically. —Yury Suleymanov Nanophotonic neural networks can be trained using on-chip backpropagation, unlocking opportunities for analog computing.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/be6db6fab0943fd776a33e840310e60f6ebc5355

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