Fu Tingzhao, Zhang Jianfa, Sun Run, Huang Yuyao, Xu Wei, Yang Sigang, Zhu Zhihong, Chen Hongwei
College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, China.
Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices, National University of Defense Technology, Changsha, China.
Light Sci Appl. 2024 Sep 20;13(1):263. doi: 10.1038/s41377-024-01590-3.
Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources, advanced algorithms, and high-performance electronic hardware. However, conventional computing hardware is inefficient at implementing complex tasks, in large part because the memory and processor in its computing architecture are separated, performing insufficiently in computing speed and energy consumption. In recent years, optical neural networks (ONNs) have made a range of research progress in optical computing due to advantages such as sub-nanosecond latency, low heat dissipation, and high parallelism. ONNs are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing paradigm. Herein, we first introduce the design method and principle of ONNs based on various optical elements. Then, we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip components. Finally, we summarize and discuss the computational density, nonlinearity, scalability, and practical applications of ONNs, and comment on the challenges and perspectives of the ONNs in the future development trends.
在大数据资源、先进算法和高性能电子硬件的助力下，人工智能已在各行各业盛行。然而，传统计算硬件在执行复杂任务时效率低下，很大程度上是因为其计算架构中的内存和处理器是分离的，在计算速度和能耗方面表现欠佳。近年来，光学神经网络（ONNs）由于具有亚纳秒级延迟、低散热和高并行性等优势，在光学计算方面取得了一系列研究进展。ONNs有望以一种新颖的计算范式为人工智能的进一步发展提供计算速度和能耗方面的支持。在此，我们首先介绍基于各种光学元件的ONNs的设计方法和原理。然后，我们依次回顾由体光学元件组成的非集成ONNs和由片上元件组成的集成ONNs。最后，我们总结并讨论ONNs的计算密度、非线性、可扩展性和实际应用，并对ONNs在未来发展趋势中的挑战和前景进行评论。
