| 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
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| | Editor/Publisher: | IEEE, | | Volume(Issue): | 24(6) p.8300310 (10 pages) | | DOI: | 10.1109/JSTQE.2018.2821843 | | Citations: | 43 (Dimensions.ai - last update: 15/12/2024)
34 (OpenCitations - last update: 27/6/2024)
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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. Related Research Topics
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