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Million Words per Second Classification (Viewpoint / Phys. Rev. X)

Reservoir Computing Speeds Up

Figure : A reservoir computer is centered on an artificial neural network (center) that consists of multiple nonlinear units. The computer undergoes a training process that involves adjusting the output connections (not shown) from the elements in the network such that the computer’s output matches a desired target. The network usually consists of many nonlinear units, which mimic neurons in the brain. Following an earlier design, Larger et al. built a reservoir computer with optoelectronic components that was instead centered on one nonlinear unit, and they showed that the machine could successfully identify speech signals at a rate of one million words per second [1]. [Credit: APS/Alan Stonebraker]

Viewpoint: Reservoir Computing Speeds Up

A brain-inspired computer made with optoelectronic parts runs faster thanks to a hardware redesign, recognizing simple speech at the rate of 1 million words per second.
Author: Miguel C. Soriano, Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain E-07122
February 6, 2017• Physics 10, 12

High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification

Laurent Larger, Antonio Baylón-Fuentes, Romain Martinenghi, Vladimir S. Udaltsov, Yanne K. Chembo, and Maxime Jacquot
Phys. Rev. X 7, 011015 – Published 6 February 2017

Reservoir computing, originally referred to as an echo state network or a liquid state machine, is a brain-inspired paradigm for processing temporal information. It involves learning a “read-out” interpretation for nonlinear transients developed by high-dimensional dynamics when the latter is excited by the information signal to be processed. This novel computational paradigm is derived from recurrent neural network and machine learning techniques. It has recently been implemented in photonic hardware for a dynamical system, which opens the path to ultrafast brain-inspired computing. We report on a novel implementation involving an electro-optic phase-delay dynamics designed with off-the-shelf optoelectronic telecom devices, thus providing the targeted wide bandwidth. Computational efficiency is demonstrated experimentally with speech-recognition tasks. State-of-the-art speed performances reach one million words per second, with very low word error rate. Additionally, to record speed processing, our investigations have revealed computing-efficiency improvements through yet-unexplored temporal-information-processing techniques, such as simultaneous multisample injection and pitched sampling at the read-out compared to information “write-in”.

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