Multidimensional fiber echo state network analogue


Abstract: Optical neuoromorphic technologies enable neural network-based signal processing through a specifically designed hardware and may confer advantages in speed and energy. However, the advances of such technologies in bandwidth and/or dimensionality are often limited by the constraints of the underlying material. Optical fiber presents a well-studied low-cost solution with unique advantages for low-loss high-speed signal processing. The fiber echo state network analogue (FESNA), fiber-based neuromorphic processor, has been the first technology suitable for multichannel high bandwidth (including THz) and dual-quadrature signal processing. Here we propose the multidimensional FESNA (MD-FESNA) processing by utilizing multi-mode fiber non-linearity. Thus, the developed MD-FESNA is the first neuromorphic technology which augments all aforementioned advantages of FESNA with multidimensional spatio-temporal processing. We demonstrate the performance and flexibility of the technology on the example of prediction tasks for hyperchaotic systems. These results will pave the way for a high-speed neuromorphic processing of multidimensional tasks, hardware for spatio-temporal neural networks and open new application venues for fiber-based spatio-temporal multiplexing.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
Additional Information: Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Funding: This project was supported by the Royal Academy of Engineering under the Research Fellowship scheme RF/201718/17154.
Uncontrolled Keywords: Fiber-optics,Neuromorphic computing,Optical signal processing,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials,Electrical and Electronic Engineering
Publication ISSN: 2515-7647
Last Modified: 19 Feb 2024 08:35
Date Deposited: 02 Oct 2020 08:13
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Related URLs: https://iopscie ... 515-7647/abb584 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-10-01
Accepted Date: 2020-09-04
Authors: Sorokina, Mariia (ORCID Profile 0000-0001-6082-0316)



Version: Published Version

License: Creative Commons Attribution

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