Prospects and applications of photonic neural networks

Advances in Physics: X

Daniel Brunner¹  Alexander N. Tait²  Paul R. Prucnal³  Chaoran Huang³  Thomas Ferreira de Lima³  Bhavin J. Shastri⁴  Bicky A. Marquez⁴  Jiahui Wang⁵  Shanhui Fan⁵  Volker J. Sorger⁶  Mario Miscuglio⁶  Mohammed Al-Qadasi⁷  Sudip Shekhar⁷  Lutz Lampe⁷  Avilash Mukherjee⁷  Mitchell Nichols⁷  Lukas Chrostowski⁷  Mable P. Fok⁸ 

[1] Institut FEMTO-ST, Université Bourgogne-Franche-Comté CNRS UMR;National Institute of Standards and Technology;Princeton University;Queen’s University;Stanford University;The George Washington University;The University of British Columbia;University of Georgia;

关键词: photonic neural networks;    neuromorphic photonics;    silicon photonics;    neuromorphic computing;    machine learning;    artificial intelligence;   

DOI  :  10.1080/23746149.2021.1981155

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown