Title: Integrated photonic neuromorphic computing: device, architecture, chip, algorithm Show Chinese title

Title: 集成光子神经形态计算：器件、架构、芯片、算法

Abstract: Artificial intelligence (AI) has experienced explosive growth in recent years. The large models have been widely applied in various fields, including natural language processing, image generation, and complex decision-making systems, revolutionizing technological paradigms across multiple industries. Nevertheless, the substantial data processing demands during model training and inference result in the computing power bottleneck. Traditional electronic chips based on the von Neumann architecture struggle to meet the growing demands for computing power and power efficiency amid the continuous development of AI. Photonic neuromorphic computing, an emerging solution in the post-Moore era, exhibits significant development potential. Leveraging the high-speed and large-bandwidth characteristics of photons in signal transmission, as well as the low-power consumption advantages of optical devices, photonic integrated computing chips have the potential to overcome the memory wall and power wall issues of electronic chips. In recent years, remarkable advancements have been made in photonic neuromorphic computing. This article presents a systematic review of the latest research achievements. It focuses on fundamental principles and novel neuromorphic photonic devices, such as photonic neurons and photonic synapses. Additionally, it comprehensively summarizes the network architectures and photonic integrated neuromorphic chips, as well as the optimization algorithms of photonic neural networks. In addition, combining with the current status and challenges of this field, this article conducts an in-depth discussion on the future development trends of photonic neuromorphic computing in the directions of device integration, algorithm collaborative optimization, and application scenario expansion, providing a reference for subsequent research in the field of photonic neuromorphic computing. Show Chinese abstract

Abstract: 人工智能（AI）近年来经历了爆炸性增长。 大型模型已被广泛应用于多个领域，包括自然语言处理、图像生成和复杂决策系统，革新了多个行业的技术范式。 然而，在模型训练和推理过程中巨大的数据处理需求导致了算力瓶颈。 基于冯·诺依曼架构的传统电子芯片在人工智能持续发展的背景下，难以满足日益增长的算力和能效需求。 光子类脑计算是后摩尔时代的一种新兴解决方案，显示出显著的发展潜力。 利用光子在信号传输中的高速和大带宽特性，以及光器件的低功耗优势，光子集成计算芯片有望克服电子芯片的存储墙和功耗墙问题。 近年来，光子类脑计算取得了显著进展。 本文对最新的研究成果进行了系统综述。 它聚焦于基本原理和新型类脑光子器件，如光子神经元和光子突触。 此外，还全面总结了网络架构和光子集成类脑芯片，以及光子神经网络的优化算法。 此外，结合该领域的现状和挑战，本文深入探讨了光子类脑计算在未来在器件集成、算法协同优化和应用场景扩展方向的发展趋势，为光子类脑计算领域的后续研究提供了参考。