Multidimensional fiber echo state network analogue

Mariia Sorokina

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Article number044006
JournalJournal of Physics: Photonics
Volume2
Issue number4
DOIs
Publication statusPublished - 1 Oct 2020

Bibliographical note

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.

Keywords

  • Fiber-optics
  • Neuromorphic computing
  • Optical signal processing

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