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

作者：Laurent Larger, A. Baylón-Fuentes, Romain Martinenghi, V. S. Udaltsov, Yanne K. Chembo, Maxime Jacquot · 发表于：Physical Review X · 年份：2017 · DOI：10.1103/physrevx.7.011015 · 被引用次数：508 · 研究领域：Neural Networks and Reservoir Computing、Advanced Memory and Neural Computing、Optical Network Technologies

Reservoir computing, originally referred to as an echo state network or a liquid state machine, is a braininspired 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 temporalinformation-processing techniques, such as simultaneous multisample injection and pitched sampling at the read-out compared to information "write-in".