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

AutorBrunner, Daniel ; Soriano, Miguel C. ; Mirasso, Claudio R. ; Fischer, Ingo
Fecha de publicación15-ene-2013
EditorNature Publishing Group
CitaciónNature Communications 4: 1364 (2013)
ResumenThe 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. © 2013 Macmillan Publishers Limited. All rights reserved.
Versión del editorhttp://dx.doi.org/10.1038/ncomms2368
Identificadoresdoi: 10.1038/ncomms2368
issn: 2041-1723
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