Literature DB >> 25607084

Integrated photonic reservoir computing based on hierarchical time-multiplexing structure.

Hong Zhang, Xue Feng, Boxun Li, Yu Wang, Kaiyu Cui, Fang Liu, Weibei Dou, Yidong Huang.   

Abstract

An integrated photonic reservoir computing (RC) based on hierarchical time-multiplexing structure is proposed by numerical simulations. A micro-ring array (MRA) is employed as a typical time delay implementation of RC. At the output port of the MRA, a secondary time-multiplexing is achieved by multi-mode interference (MMI) splitter and delay line array. This hierarchical time-multiplexing structure can ensure a large reservoir size with fast processing speed. Simulation results indicate that the proposed RC system yields better performance than previously reported ones. The achieved normalized mean square error between the system output and target sequence are 0.5% and 2.7% for signal classification and chaotic time series prediction, respectively, while the sample rate is as high as 1.3 Gbps.

Year:  2014        PMID: 25607084     DOI: 10.1364/OE.22.031356

Source DB:  PubMed          Journal:  Opt Express        ISSN: 1094-4087            Impact factor:   3.894


  4 in total

Review 1.  Minimal approach to neuro-inspired information processing.

Authors:  Miguel C Soriano; Daniel Brunner; Miguel Escalona-Morán; Claudio R Mirasso; Ingo Fischer
Journal:  Front Comput Neurosci       Date:  2015-06-02       Impact factor: 2.380

2.  Low-Loss Photonic Reservoir Computing with Multimode Photonic Integrated Circuits.

Authors:  Andrew Katumba; Jelle Heyvaert; Bendix Schneider; Sarah Uvin; Joni Dambre; Peter Bienstman
Journal:  Sci Rep       Date:  2018-02-08       Impact factor: 4.379

3.  Fully analogue photonic reservoir computer.

Authors:  François Duport; Anteo Smerieri; Akram Akrout; Marc Haelterman; Serge Massar
Journal:  Sci Rep       Date:  2016-03-03       Impact factor: 4.379

4.  Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations.

Authors:  Massimo Borghi; Stefano Biasi; Lorenzo Pavesi
Journal:  Sci Rep       Date:  2021-08-02       Impact factor: 4.379

  4 in total

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