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.

中文翻译：

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大规模光子递归神经网络中的强化学习

作为潜在的破坏性未来技术，光子神经网络的实现已引起了广泛的关注。在大规模神经网络中演示学习对于将光子机器学习基质建立为可行的信息处理系统至关重要。迄今为止，尚缺乏在完全并行且高效的学习硬件中实现具有众多非线性节点的光子神经网络的功能。我们演示了多达2025个衍射耦合光子节点的网络，形成了大规模的递归神经网络。使用数字微镜设备，我们可以实现强化学习。我们的方案是完全并行的，并且无源权重使能量效率和带宽最大化。计算输出有效地收敛，并且我们获得了非常好的性能。