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2 March 2022 Silicon photonic neural network applications and prospects
Bhavin J. Shastri, Chaoran Huang, Alexander N. Tait, Thomas Ferreira de Lima, Paul R. Prucnal
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Proceedings Volume 12019, AI and Optical Data Sciences III; 120190K (2022) https://doi.org/10.1117/12.2614865
Event: SPIE OPTO, 2022, San Francisco, California, United States
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.
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Bhavin J. Shastri, Chaoran Huang, Alexander N. Tait, Thomas Ferreira de Lima, and Paul R. Prucnal "Silicon photonic neural network applications and prospects", Proc. SPIE 12019, AI and Optical Data Sciences III, 120190K (2 March 2022); https://doi.org/10.1117/12.2614865
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KEYWORDS
Photonics
Neural networks
Machine learning
Silicon photonics
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