Reinforcement learning in a large-scale photonic recurrent neural network
Bueno, J. and Maktoobi, S. and Froehly, L. and Fischer, I. and Jacquot, M. and Larger, L. and Brunner, D. (2018) Reinforcement learning in a large-scale photonic recurrent neural network. Optica, 5 (6). pp. 756-760. ISSN 1899-7015
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Abstract
Photonic neural network implementation has been gaining considerable attention as a potentially disruptive future technology. Demonstrating learning in large-scale neural networks is essential to establish photonic machine learning substrates as viable information processing systems. Realizing photonic neural networks with numerous nonlinear nodes in a fully parallel and efficient learning hardware has been lacking so far. We demonstrate a network of up to 2025 diffractively coupled photonic nodes, forming a large-scale recurrent neural network. Using a digital micro mirror device, we realize reinforcement learning. Our scheme is fully parallel, and the passive weights maximize energy efficiency and bandwidth. The computational output efficiently converges, and we achieve very good performance.
Item type: Article ID code: 69578 Dates: DateEvent20 June 2018Published21 May 2018AcceptedKeywords: neural networks, nonlinear optics, optical neural systems, Optics. Light, Electronic, Optical and Magnetic Materials, Atomic and Molecular Physics, and Optics Subjects: Science > Physics > Optics. Light Department: Faculty of Science > Physics > Institute of Photonics Depositing user: Pure Administrator Date deposited: 02 Sep 2019 14:19 Last modified: 19 Mar 2021 04:35 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/69578
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