Abstract
Reservoir computing architectures offer important benefits for the implementation of a neural network in a physical medium, as the weighted interconnections between the internal nodes are random and fixed. Experimental results on a time-delay photonic reservoir computer based on directly modulated Vertical Cavity Surface Emitting Lasers and multi-mode fiber couplers are presented. The neuron is made of photodiode, non-linear amplifier and laser chips. The NARMA10 chaotic time-series task is performed with a configuration having 25 virtual nodes operating at 1 GS/s. Experimental and simulated error ranges are in good agreement, which is promising for an expansion to a more elaborate system. The potential of this scheme for the realization of a photonic reservoir cluster device operating at very high speed with low power and a small footprint with a large number of interacting physical and virtual neurons is discussed.
Supported by the New Energy Development Organization (NEDO). P.A. acknowledges financial support by the founders of the Chaire Photonique.
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Héroux, J.B., Kanazawa, N., Antonik, P. (2019). Time Series Processing with VCSEL-Based Reservoir Computer. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_17
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DOI: https://doi.org/10.1007/978-3-030-30493-5_17
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