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Photonic reservoir computing enabled by stimulated Brillouin scattering

Phang, Sendy

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Abstract

Artificial intelligence (AI) drives the creation of future technologies that disrupt the way humans live and work, creating new solutions that change the way we approach tasks and activities, but it requires a lot of data processing, large amounts of data transfer, and computing speed. It has led to a growing interest of research in developing a new type of computing platform which is inspired by the architecture of the brain specifically those that exploit the benefits offered by photonic technologies, fast, low-power, and larger bandwidth. Here, a new computing platform based on the photonic reservoir computing architecture exploiting the non-linear wave-optical dynamics of the stimulated Brillouin scattering is reported. The kernel of the new photonic reservoir computing system is constructed of an entirely passive optical system. Moreover, it is readily suited for use in conjunction with high performance optical multiplexing techniques to enable real-time artificial intelligence. Here, a methodology to optimise the operational condition of the new photonic reservoir computing is described which is found to be strongly dependent on the dynamics of the stimulated Brillouin scattering system. The new architecture described here offers a new way of realising AI-hardware which highlight the application of photonics for AI.

Citation

Phang, S. (2023). Photonic reservoir computing enabled by stimulated Brillouin scattering. Optics Express, 31(13), 22061-22074. https://doi.org/10.1364/oe.489057

Journal Article Type Article
Acceptance Date Jun 8, 2023
Online Publication Date Jun 15, 2023
Publication Date Jun 19, 2023
Deposit Date Jun 14, 2023
Publicly Available Date Jun 27, 2023
Journal Optics Express
Electronic ISSN 1094-4087
Publisher Optical Society of America
Peer Reviewed Peer Reviewed
Volume 31
Issue 13
Pages 22061-22074
DOI https://doi.org/10.1364/oe.489057
Keywords Atomic and Molecular Physics, and Optics
Public URL https://nottingham-repository.worktribe.com/output/21910366
Publisher URL https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-13-22061&id=531780

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