Parallel photonic information processing at gigabyte per second data rates using transient states

Daniel Brunner (), Miguel C. Soriano, Claudio R. Mirasso and Ingo Fischer
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Daniel Brunner: Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC)
Miguel C. Soriano: Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC)
Claudio R. Mirasso: Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC)
Ingo Fischer: Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC)

Nature Communications, 2013, vol. 4, issue 1, 1-7

Abstract: Abstract The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

Date: 2013
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DOI: 10.1038/ncomms2368

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