| Issue |
Photoniques
Number 104, Septembre-Octobre 2020
|
|
|---|---|---|
| Page(s) | 45 - 48 | |
| Section | Focus: Photonics and Artificial intelligence | |
| DOI | https://doi.org/10.1051/photon/202010445 | |
| Published online | 09 November 2020 | |
Photonic reservoir computing using delay dynamical systems
1
LMOPS EA 4423 Laboratory & Chair in Photonics, Centrale-Supélec & Université de Lorraine, 2 rue Edouard Belin, Metz, France
2
Laboratoire d’Information Quantique, CP224, Université libre de Bruxelles, Av. F. D. Roosevelt 50, Bruxelles, Belgium
3
Applied Physics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, Belgium
* e-mail : smassar@ulb.ac.be
The recent progress in artificial intelligence has spurred renewed interest in hardware implementations of neural networks. Reservoir computing is a powerful, highly versatile machine learning algorithm well suited for experimental implementations. The simplest highperformance architecture is based on delay dynamical systems. We illustrate its power through a series of photonic examples, including the first all optical reservoir computer and reservoir computers based on lasers with delayed feedback. We also show how reservoirs can be used to emulate dynamical systems. We discuss the perspectives of photonic reservoir computing.
© The authors, published by EDP Sciences, 2020
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