There has been a resurgence of interest in optical computing over the past decade, both in academia and in industry, with much of the excitement centered around special-purpose optical computers for neural-network processing. Optical computing has been a topic of periodic study for over 50 years, including for neural networks three decades ago, and a wide variety of optical-computing schemes and architectures have been proposed. In this paper we provide a systematic explanation of why and how optics might be able to give speed or energy-efficiency benefits over electronics for computing, enumerating 11 features of optics that can be harnessed when designing an optical computer. One often-mentioned motivation for optical computing -- that the speed of light $c$ is fast -- is not a key differentiating physical property of optics for computing; understanding where an advantage could come from is more subtle. We discuss how gaining an advantage over state-of-the-art electronic processors will likely only be achievable by careful design that harnesses more than one of the 11 features, while avoiding a number of pitfalls that we describe.

翻译：过去十年间，光学计算在学术界和工业界重新激起研究热潮，其中围绕专用神经网络处理光学计算机的研究尤为引人注目。光学计算作为一个周期性研究课题已逾50年，包括三十年前对神经网络的应用探索，学界已提出种类繁多的光学计算方案与架构。本文系统阐释了光学计算相较于电子计算在速度或能效方面可能具备优势的机理，归纳出设计光学计算机时可资利用的11项光学特性。一个常被提及的动机——光速$c$极快——并非光学计算区别于电子计算的关键物理特性；理解优势的真正来源需要更为精微的洞察。研究表明，要在现有电子处理器基础上实现性能突破，必须通过精心设计同时运用多项光学特性（本文所述11项中至少两项），同时规避我们指出的若干技术陷阱。