Machine Learning With Neuromorphic Photonics

Abstract

Neuromorphic photonics has experienced a recent surge of interest over the last few years, promising orders of magnitude improvements in both speed and energy efficiency over digital electronics. This paper provides a tutorial overview of neuromorphic photonic systems and their application to optimization and machine learning problems. We discuss the physical advantages of photonic processing systems, and we describe underlying device models that allow practical systems to be constructed. We also describe several real-world applications for control and deep learning inference. Finally, we discuss scalability in the context of designing a full-scale neuromorphic photonic processing system, considering aspects such as signal integrity, noise, and hardware fabrication platforms. The paper is intended for a wide audience and teaches how theory, research, and device concepts from neuromorphic photonics could be applied in practical machine learning systems.