Reinforcement learning in a large-scale photonic recurrent neural network
Reinforcement learning in a large-scale photonic recurrent neural network
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DOI：
10.1364/optica.5.000756
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发表时间：
2018-06-20
期刊：
影响因子：
10.4
通讯作者：
Brunner, D.
中科院分区：
文献类型：
--
作者：
Bueno, J.;Maktoobi, S.;Brunner, D.
光子神经网络实现作为一种潜在的颠覆性未来技术已经获得了相当大的关注。在大规模神经网络中演示学习对于将光子机器学习基底建立为可行的信息处理系统至关重要。迄今为止，在完全并行和有效的学习硬件中实现具有许多非线性节点的光子神经网络一直缺乏。我们展示了一个多达2025个衍射耦合光子节点的网络，形成了一个大规模的递归神经网络。利用数字微镜器件实现了强化学习。我们的方案是完全并行的，被动权重最大限度地提高能源效率和带宽。计算输出有效地收敛，我们实现了非常好的性能。(C)根据OSA开放获取出版协议的条款，2018年美国光学学会
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. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement