Prospects and applications of photonic neural networks

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 eff...

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Published inAdvances in physics: X Vol. 7; no. 1
Main 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.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 01.01.2022
Taylor & Francis Ltd
Taylor & Francis Group
Subjects
Acceleration
Algorithms
Artificial intelligence
Bandwidths
Brain
Computer engineering
Computers
Digital computers
Free space optics
Machine learning
Microprocessors
Neural networks
Neuromorphic computing
neuromorphic photonics
Nonlinear programming
Parallel processing
Photonic neural networks
Photonics
Physics
Signal processing
silicon photonics
Synapses
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Summary: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.
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ISSN:2374-6149
2374-6149
DOI:10.1080/23746149.2021.1981155