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Proceedings Paper

Deep learning with synthetic photonic lattices for equalization in optical transmission systems
Author(s): Artem V. Pankov; Oleg S. Sidelnikov; Ilya D. Vatnik; Andrey A. Sukhorukov; Dmitriy V. Churkin
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Paper Abstract

In this work we propose a new physical realization of optical neural network (ONN) based on a recently appeared technological platform of synthetic photonic lattices (SPL), and demonstrate its capabilities for deep learning. The system operates with time series of optical pulses with ability to easily control their parameters and possesses the architecture that well suits the ONN paradigm. We have also shown that such an ONN can be potentially utilized for signal processing in optical communication lines for signal distortion compensation.

Paper Details

Date Published: 20 November 2019
PDF: 11 pages
Proc. SPIE 11192, Real-time Photonic Measurements, Data Management, and Processing IV, 111920N (20 November 2019); doi: 10.1117/12.2537462
Show Author Affiliations
Artem V. Pankov, Novosibirsk State Univ. (Russian Federation)
Oleg S. Sidelnikov, Novosibirsk State Univ. (Russian Federation)
Institute of Computational Technologies (Russian Federation)
Ilya D. Vatnik, Novosibirsk State Univ. (Russian Federation)
Institute of Automation and Electrometry (Russian Federation)
Andrey A. Sukhorukov, The Australian National Univ. (Australia)
Dmitriy V. Churkin, Novosibirsk State Univ. (Russian Federation)
Institute of Automation and Electrometry (Russian Federation)


Published in SPIE Proceedings Vol. 11192:
Real-time Photonic Measurements, Data Management, and Processing IV
Ming Li; Bahram Jalali; Mohammad Hossein Asghari, Editor(s)

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