Journal
ADVANCES IN PHYSICS-X
Volume 7, Issue 1, Pages -Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/23746149.2021.1981155
Keywords
Photonic neural networks; neuromorphic photonics; silicon photonics; neuromorphic computing; machine learning; artificial intelligence
Categories
Funding
- Canadian Foundation for Innovation [37780]
- Natural Sciences and Engineering Research Council of Canada [RGPIN-2018-05249]
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Neuromorphic engineering using photonics allows for high-speed and energy-efficient artificial intelligence and neuromorphic computing applications. It mimics neurons and synapses in the brain to achieve distributed and parallel processing.
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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