Neuromorphic computing based on silicon photonics

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dc.contributor.authorKatumba, Andrew
dc.contributor.authorFreiberger, Matthias
dc.contributor.authorLaporte, Floris
dc.contributor.authorLugnan, Alessio
dc.contributor.authorSackesyn, Stijn
dc.contributor.authorMa, Chonghuai
dc.contributor.authorDambre, Joni
dc.contributor.authorBienstman, Peter
dc.date.accessioned2022-11-30T20:28:36Z
dc.date.available2022-11-30T20:28:36Z
dc.date.issued2018
dc.description.abstractWe 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.en_US
dc.identifier.citationKatumba, A., Freiberger, M., Laporte, F., Lugnan, A., Sackesyn, S., Ma, C., ... & Bienstman, P. (2018). Neuromorphic computing based on silicon photonics and reservoir computing. IEEE Journal of Selected Topics in Quantum Electronics, 24(6), 1-10.en_US
dc.identifier.urihttps://ieeexplore.ieee.org/abstract/document/8331848/
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/5584
dc.language.isoenen_US
dc.publisherIEEE Journal of Selected Topics in Quantum Electronicsen_US
dc.subjectSilicon photonicsen_US
dc.subjectNeuromorphic computingen_US
dc.subjectReservoir computingen_US
dc.titleNeuromorphic computing based on silicon photonicsen_US
dc.typeArticleen_US
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