强化学习
光子学
人工神经网络
计算机科学
带宽(计算)
实施
非线性系统
循环神经网络
人工智能
分布式计算
电信
材料科学
光电子学
量子力学
物理
程序设计语言
作者
Julián Bueno,Sheler Maktoobi,Luc Froehly,Ingo Fischer,Maxime Jacquot,Laurent Larger,Daniel Brunner
出处
期刊:Optica
[The Optical Society]
日期:2018-06-20
卷期号:5 (6): 756-756
被引量:253
标识
DOI:10.1364/optica.5.000756
摘要
Photonic Neural Network implementations have 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 was lacking so far. We demonstrate a network of up to 2500 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.
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