The rapid development of artificial intelligence (AI) facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data. Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth, low latency, and high energy efficiency. In this review, we introduce the latest developments of optical computing for different AI models, including feedforward neural networks, reservoir computing, and spiking neural networks (SNNs). Recent progress in integrated photonic devices, combined with the rise of AI, provides a great opportunity for the renaissance of optical computing in practical applications. This effort requires multidisciplinary efforts from a broad community. This review provides an overview of the state-of-the-art accomplishments in recent years, discusses the availability of current technologies, and points out various remaining challenges in different aspects to push the frontier. We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.

人工智能（AI）的快速发展促进了各个领域的各种应用，但由于数据的爆炸式增长，在速度和能量方面也对其硬件实施提出了巨大挑战。光学计算通过利用光子的独特属性（包括宽带宽、低延迟和高能效）为解决这一瓶颈提供了独特的视角。在这篇综述中，我们介绍了针对不同 AI 模型的光学计算的最新进展，包括前馈神经网络、水库计算和尖峰神经网络 (SNN)。集成光子器件的最新进展，加上人工智能的兴起，为光学计算在实际应用中的复兴提供了巨大的机会。这项工作需要广泛社区的多学科努力。本综述概述了近年来的最新成就，讨论了当前技术的可用性，并指出了推动前沿的不同方面的各种剩余挑战。我们预计，大规模集成光子处理器的时代将很快以混合光电框架的形式用于实际的人工智能应用。