@inproceedings{0731a8499f9946688a3eab36396570be,
title = "Silicon Photonic Neural Applications and Prospects",
abstract = "Neural networks have enabled many applications in artificial intelligence and neuromorphic computing ranging from scientific computing, intelligent communications, security etc. Neural networks implemented in on digital platforms are limited in speed and energy efficiency. Neuromorphic (i.e., neuron-isomorphic) photonics aims to build processors in which optical hardware mimic neural networks in the brain. These processors promise orders of magnitude improvements in both speed and energy efficiency over purely digital electronic approaches. However, integrated optical neural networks are much smaller (hundreds of neurons) than electronic implementations (tens of millions of neurons). This raises a question: what are the applications where sub-nanosecond latencies and energy efficiency trump the sheer size of processor? We provide an overview of neuromorphic photonic systems and their real-world applications to machine learning and neuromorphic computing.",
keywords = "Silicon photonics, machine learning, neuromorphic computing, photonic integrated circuits",
author = "Shastri, {Bhavin J.} and Chaoran Huang and Tait, {Alexander N.} and {de Lima}, {Thomas Ferreira} and Prucnal, {Paul R.}",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE; AI and Optical Data Sciences III 2022 ; Conference date: 20-02-2022 Through 24-02-2022",
year = "2022",
doi = "10.1117/12.2614865",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Bahram Jalali and Ken-ichi Kitayama",
booktitle = "AI and Optical Data Sciences III",
address = "United States",
}