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Proceedings Paper

Silicon photonics for neuromorphic information processing
Author(s): Peter Bienstman; Joni Dambre; Andrew Katumba; Matthias Freiberger; Floris Laporte; Alessio Lugnan
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Paper Abstract

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 efficiency, 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.

Paper Details

Date Published: 14 February 2018
PDF: 7 pages
Proc. SPIE 10551, Optical Data Science: Trends Shaping the Future of Photonics, 105510K (14 February 2018);
Show Author Affiliations
Peter Bienstman, Univ. Gent (Belgium)
Joni Dambre, Univ. Gent (Belgium)
Andrew Katumba, Univ. Gent (Belgium)
Matthias Freiberger, Univ Gent. (Belgium)
Floris Laporte, Univ. Gent (Belgium)
Alessio Lugnan, Univ. Gent (Belgium)

Published in SPIE Proceedings Vol. 10551:
Optical Data Science: Trends Shaping the Future of Photonics
Bahram Jalali; Ken-ichi Kitayama; Ata Mahjoubfar, Editor(s)

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