Embodiment of Learning in Electro-Optical Signal Processors

Michiel Hermans, Piotr Antonik, Marc Haelterman, and Serge Massar
Phys. Rev. Lett. 117, 128301 – Published 16 September 2016
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Abstract

Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular, it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here, we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design. We find that, compared to when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on three benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.

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  • Received 4 March 2016

DOI:https://doi.org/10.1103/PhysRevLett.117.128301

© 2016 American Physical Society

Physics Subject Headings (PhySH)

Atomic, Molecular & OpticalInterdisciplinary PhysicsNonlinear Dynamics

Authors & Affiliations

Michiel Hermans1,*, Piotr Antonik1,†, Marc Haelterman2, and Serge Massar1

  • 1Laboratoire d’Information Quantique, Université libre de Bruxelles, 50 Avenue F. D. Roosevelt, CP 224, B-1050 Brussels, Belgium
  • 2Service OPERA-Photonique, Université libre de Bruxelles, 50 Avenue F. D. Roosevelt, CP 194/5, B-1050 Brussels, Belgium

  • *michiel.hermans@ulb.ac.be
  • piotr.antonik@ulb.ac.be

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Issue

Vol. 117, Iss. 12 — 16 September 2016

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