Machine Learning Assisted Management of Photonic Switching Systems

Ihtesham Khan; M Umar Masood; Lorenzo Tunesi; Paolo Bardella; Enrico Ghillino; Andrea Carena; Vittorio Curri

doi:10.1364/CLEO_AT.2021.JTu3A.32

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper JTu3A.32
•https://doi.org/10.1364/CLEO_AT.2021.JTu3A.32

Machine Learning Assisted Management of Photonic Switching Systems

Ihtesham Khan, M Umar Masood, Lorenzo Tunesi, Paolo Bardella, Enrico Ghillino, Andrea Carena, and Vittorio Curri

CLEO: QELS_Fundamental Science 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Joint Poster Session I (JTu3A)

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I. Khan, M. U. Masood, L. Tunesi, P. Bardella, E. Ghillino, A. Carena, and V. Curri, "Machine Learning Assisted Management of Photonic Switching Systems," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JTu3A.32.

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Abstract

We propose a machine learning based approach to predict control signals for a photonic switching system. This solution is topology and technology agnostic and it can be employed in real-time switch control planes.

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