Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and that operate sequentially) are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimics neurons and synapses in the brain for distributed and parallel processing. Neuromorphic engineering enabled by photonics (optical physics) can offer sub-nanosecond latencies and high bandwidth with low energies to extend the domain of artificial intelligence and neuromorphic computing applications to machine learning acceleration, nonlinear programming, intelligent signal processing, etc. Photonic neural networks have been demonstrated on integrated platforms and free-space optics depending on the class of applications being targeted. Here, we discuss the prospects and demonstrated applications of these photonic neural networks.

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光子神经网络的前景与应用

神经网络已通过机器学习和神经形态计算实现了在人工智能中的应用。具有独立的内存和处理器（并顺序运行）的常规计算机上的神经网络软件实现在速度和能源效率上受到限制。神经形态工程旨在构建处理器，其中硬件模仿大脑中的神经元和突触，以进行分布式和并行处理。由光子学（光学物理学）支持的神经形态工程可以提供亚纳秒级的延迟和低能量的高带宽，从而将人工智能和神经形态计算应用的领域扩展到机器学习加速，非线性编程，智能信号处理等领域。根据目标应用的类别，已经在集成平台和自由空间光学系统上演示了光子神经网络。在这里，我们讨论了这些光子神经网络的前景并演示了它们的应用。