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

By Daniel Brunner, Miguel C. Soriano, Claudio R. Mirasso, Ingo Fischer. Nature Communications, 2013

812 citations recorded by the source corpus.

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. Inspired by neural networks, reservoir computing uses nonlinear transient states to perform computations, offering faster parallel information processing. Brunner et al.show a photonic approach to reservoir computing capable of simultaneous spoken digit and speaker recognition at high data rates.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3b61e87827376fd76b774f349cbfe9012737a0a0

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Citations in the corpus

Quantum machine learning (2016, positive): “Brunner et al. used photonics to demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond a giga-byte per second [103].”
All-optical machine learning using diffractive deep neural networks (2018, background): “Being quite different in its physical operation principles compared to previous opto-electronics based deep neural networks ( 15, 18 - 20 ), the D 2 NN framework provides a unique all-optical deep learning engine that efficiently operates at the speed of light using passive components and optical…”
Inference in artificial intelligence with deep optics and photonics (2020, background): “4, 5).”
Emerging opportunities and challenges for the future of reservoir computing (2024, background): “Reservoir networks based on optical systems have also made signi ﬁ cant progress 81 , such as multi-scattering nodes in free space 32 , single nonlinear nodes with ﬁ ber loop 58 , and integrated on-chip reservoirs 66 .”
High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification (2017, methodology): “It is an attractive hardware solution, especially when photonic implementation is concerned, for which high-speed optical telecom devices can potentially provide unprecedented processing speed [6 – 9].”
Photonics for artificial intelligence and neuromorphic computing (2020, background): “Research in photonic RC has realized dynamical complexity by combining optical delays with one of either a photodetector and modulator [92], optical amplifiers [94], a semiconductor nonlinearity [95], or a recurrent interferometer mesh [96].”