Photonic reservoir computing enabled by stimulated Brillouin scattering
Sendy Phang

TL;DR
This paper introduces a novel photonic reservoir computing platform utilizing stimulated Brillouin scattering, enabling high-speed, low-power AI hardware with potential for real-time applications through passive optical systems.
Contribution
It presents a new photonic reservoir computing architecture based on stimulated Brillouin scattering, optimized for real-time AI processing using passive optical components.
Findings
Achieved a passive optical reservoir computing system using stimulated Brillouin scattering.
Optimized operational conditions based on scattering dynamics.
Demonstrated potential for real-time AI applications with high bandwidth.
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…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Photonic and Optical Devices · Optical Network Technologies
