Cheng Junwei, Zhou Hailong, Dong Jianji
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Nanomaterials (Basel). 2021 Jun 26;11(7):1683. doi: 10.3390/nano11071683.
In emerging artificial intelligence applications, massive matrix operations require high computing speed and energy efficiency. Optical computing can realize high-speed parallel information processing with ultra-low energy consumption on photonic integrated platforms or in free space, which can well meet these domain-specific demands. In this review, we firstly introduce the principles of photonic matrix computing implemented by three mainstream schemes, and then review the research progress of optical neural networks (ONNs) based on photonic matrix computing. In addition, we discuss the advantages of optical computing architectures over electronic processors as well as current challenges of optical computing and highlight some promising prospects for the future development.
在新兴的人工智能应用中,大规模矩阵运算需要高计算速度和能源效率。光学计算可以在光子集成平台或自由空间中以超低能耗实现高速并行信息处理,这能够很好地满足这些特定领域的需求。在本综述中,我们首先介绍由三种主流方案实现的光子矩阵计算原理,然后回顾基于光子矩阵计算的光学神经网络(ONN)的研究进展。此外,我们讨论光学计算架构相对于电子处理器的优势以及光学计算当前面临的挑战,并突出一些未来发展的前景。