By the same authors

Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publicationUCNC 2021: Unconventional Computation and Natural Computation
DatePublished - 11 Oct 2021
Pages19
Number of pages34
PublisherSpringer
Volume12984
Original languageEnglish
ISBN (Electronic)978-3-030-87993-8
ISBN (Print)978-3-030-87992-1

Publication series

NameLNTCS
PublisherSpringer
Number1
Volume12984

Abstract

Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged under the name of physical reservoir computing. In this paradigm, an input-driven dynamical system (the reservoir) is exploited and trained to perform computational tasks. Recent spintronic thin-film reservoirs show state-of-the-art performances despite simplicity in their design. Here, we explore film geometry and show that simple changes to film shape and input location can lead to greater memory and improved performance across various time-series tasks.

    Research areas

  • magnetic thin films, magnetic materials, unconventional computing, reservoir computing, in-materio computing, substrate geometry

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