Browsing by Author "Laporte, Floris"

Now showing 1 - 4 of 4
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    Neuromorphic computing based on silicon photonics
    (IEEE Journal of Selected Topics in Quantum Electronics, 2018) Katumba, Andrew; Freiberger, Matthias; Laporte, Floris; Lugnan, Alessio; Sackesyn, Stijn; Ma, Chonghuai; Dambre, Joni; Bienstman, Peter
    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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    Neuromorphic information processing using silicon photonics
    (IEEE Journal of Selected Topics in Quantum Electronics, 2018) Bienstman, Peter; Dambre, Joni; Katumba, Andrew; Freiberger, Matthias; Laporte, Floris
    We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. First, we dicuss how passive reservoir computing can be used to perform non-linear signal equalisation in telecom links. Then, we introduce a training method that can deal with limited weight resolution for a hardware implementation of a photonic readout.
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    Numerical demonstration of neuromorphic computing with photonic crystal cavities
    (Optics express, 2018) Laporte, Floris; Katumba, Andrew; Dambre, Joni; Bienstman, Peter
    We propose a new design for a passive photonic reservoir computer on a silicon photonics chip which can be used in the context of optical communication applications, and study it through detailed numerical simulations. The design consists of a photonic crystal cavity with a quarter-stadium shape, which is known to foster interesting mixing dynamics. These mixing properties turn out to be very useful for memory-dependent optical signal processing tasks, such as header recognition. The proposed, ultra-compact photonic crystal cavity exhibits a memory of up to 6 bits, while simultaneously accepting bitrates in a wide region of operation. Moreover, because of the inherent low losses in a high-Q photonic crystal cavity, the proposed design is very power efficient.
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    Silicon photonics for neuromorphic information processing
    (SPIE, 2018) Bienstman, Peter; Dambre, Joni; Katumba, Andrew; Freiberger, Matthias; Laporte, Floris; Lugnan, Alessio
    We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. We will discuss aspects like scalability, novel architectures for enhanced power e ciency, as well as all-optical readout. Additionally, we will touch upon new machine learning techniques to operate these integrated readouts. Finally, we will show how these systems can be used for high-speed low-power information processing for applications like recognition of biological cells.