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Authors: A. Katumba, M. Freiberger, F. Laporte, A. Lugnan, S. Sackesyn, C. Ma, J. Dambre, P. Bienstman
Title: Neuromorphic computing based on silicon photonics and reservoir computing
Format: International Journal
Publication date: 11/2018
Journal/Conference/Book: IEEE Journal on Selected Topics in Quantum Electronics (invited)
Editor/Publisher: IEEE, 
Volume(Issue): 24(6) p.8300310 (10 pages)
DOI: 10.1109/JSTQE.2018.2821843
Citations: 26 (Dimensions.ai - last update: 3/7/2022)
6 (OpenCitations - last update: 19/4/2022)
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Abstract

We present our latest progress using new neuromorphic paradigms for optical information processing in silicon photonics. We show how passive reservoir computing chips can be used to perform a variety of tasks (bit level tasks, nonlinear dispersion compensation, ...) at high speeds and low power consumption. In addition, we present a spatial analog of reservoir computing based on pillar scatterers and a cavity, that can be used to speed up classification of biological cells.

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