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

• 成果类型: Article
• 署名作者: Pai, Sunil; Sun, Zhanghao; Hughes, Tyler W.; Park, Taewon; Bartlett, Ben; Williamson, Ian A. D.; Minkov, Momchil; Milanizadeh, Maziyar; Abebe, Nathnael; Morichetti, Francesco; Melloni, Andrea; Fan, Shanhui; Solgaard, Olav; Miller, David A. B.
• 署名单位: Stanford University; Stanford University; Polytechnic University of Milan; Alphabet Inc.; Google Incorporated
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-8234
• DOI: 10.1126/science.ade8450
• 发表日期: 2023-04-28
• 页码: 398-403
• 关键词: processor

摘要

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.