Prospects and applications of photonic neural networks. Issue 1 (1st January 2022)

• Record Type: Journal Article
• Authors: Huang, Chaoran; Sorger, Volker J.; Miscuglio, Mario; Al-Qadasi, Mohammed; Mukherjee, Avilash; Lampe, Lutz; Nichols, Mitchell; Tait, Alexander N.; Ferreira de Lima, Thomas; Marquez, Bicky A.; Wang, Jiahui; Chrostowski, Lukas; Fok, Mable P.; Brunner, Daniel; Fan, Shanhui; Shekhar, Sudip; Prucnal, Paul R.; Shastri, Bhavin J.
• Is Part Of: Advances in physics: X. Volume 7:Issue 1(2022)
• Publication Date: 2022-01-01
• Subjects: Photonic neural networks -- neuromorphic photonics -- silicon photonics -- neuromorphic computing -- machine learning -- artificial intelligence; Physics -- Periodicals; 530.05
• DOI: 10.1080/23746149.2021.1981155
• Languages: English
• ISSNs: 2374-6149

Abstract

ABSTRACT: Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and that operate sequentially) are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimics neurons and synapses in the brain for distributed and parallel processing. Neuromorphic engineering enabled by photonics (optical physics) can offer sub-nanosecond latencies and high bandwidth with low energies to extend the domain of artificial intelligence and neuromorphic computing applications to machine learning acceleration, nonlinear programming, intelligent signal processing, etc. Photonic neural networks have been demonstrated on integrated platforms and free-space optics depending on the class of applications being targeted. Here, we discuss the prospects and demonstrated applications of these photonic neural networks. Graphical Abstract: uf0001