Advances in photonic reservoir computing

Nanophotonics

article

Guy Van der Sande¹  Daniel Brunner²  Miguel C. Soriano³ 

[1] Applied Physics Research Group (APHY), Vrije Universiteit Brussel (VUB);UMR CNRS FEMTO-ST 6174/Optics Department, Université de Bourgogne Franche-Comté;Instituto de Física Interdisciplinar y Sistemas Complejos, Campus Universitat de les Illes Balears

关键词: analogue computing;    artificial neural networks;    nonlinear optics;    optical computing;   

DOI  :  10.1515/nanoph-2016-0132

学科分类：社会科学、人文和艺术（综合）

来源: De Gruyter

【 摘 要 】

We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir’s complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

【 授权许可】

CC BY