Prospects and applications of photonic neural networks
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.
Keywords
Cite
@article{arxiv.2105.09943,
title = {Prospects and applications of photonic neural networks},
author = {Chaoran Huang and Volker J. Sorger and Mario Miscuglio and Mohammed Al-Qadasi and Avilash Mukherjee and Sudip Shekhar and Lukas Chrostowski and Lutz Lampe and Mitchell Nichols and Mable P. Fok and Daniel Brunner and Alexander N. Tait and Thomas Ferreira de Lima and Bicky A. Marquez and Paul R. Prucnal and Bhavin J. Shastri},
journal= {arXiv preprint arXiv:2105.09943},
year = {2021}
}