Stanford University, Stanford, CA, USA.
University of California, Los Angeles, Los Angeles, CA, USA.
Nature. 2020 Dec;588(7836):39-47. doi: 10.1038/s41586-020-2973-6. Epub 2020 Dec 2.
Artificial intelligence tasks across numerous applications require accelerators for fast and low-power execution. Optical computing systems may be able to meet these domain-specific needs but, despite half a century of research, general-purpose optical computing systems have yet to mature into a practical technology. Artificial intelligence inference, however, especially for visual computing applications, may offer opportunities for inference based on optical and photonic systems. In this Perspective, we review recent work on optical computing for artificial intelligence applications and discuss its promise and challenges.
人工智能任务在众多应用中都需要加速器来实现快速、低功耗的执行。光学计算系统可能能够满足这些特定领域的需求,但尽管研究了半个世纪,通用光学计算系统仍未成熟为一种实用技术。然而,人工智能推断,特别是对于视觉计算应用,可能为基于光学和光子系统的推断提供机会。在这篇观点文章中,我们回顾了最近在人工智能应用的光学计算方面的工作,并讨论了其前景和挑战。