By the same authors

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

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

Standard

Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics. / Dale, Matthew; O'Keefe, Simon; Sebald, Angelika; Stepney, Susan; Trefzer, Martin Albrecht.

UCNC 2021: Unconventional Computation and Natural Computation. Vol. 12984 Springer, 2021. p. 19 (LNTCS; Vol. 12984, No. 1).

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

Harvard

Dale, M, O'Keefe, S, Sebald, A, Stepney, S & Trefzer, MA 2021, Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics. in UCNC 2021: Unconventional Computation and Natural Computation. vol. 12984, LNTCS, no. 1, vol. 12984, Springer, pp. 19, Unconventional Computation and Natural Computation 2021
19th International Conference, UCNC 2021, Espoo, Finland, October 18–22, 2021, Espoo, Finland, 18/10/21. https://doi.org/10.1007/978-3-030-87993-8_2

APA

Dale, M., O'Keefe, S., Sebald, A., Stepney, S., & Trefzer, M. A. (2021). Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics. In UCNC 2021: Unconventional Computation and Natural Computation (Vol. 12984, pp. 19). (LNTCS; Vol. 12984, No. 1). Springer. https://doi.org/10.1007/978-3-030-87993-8_2

Vancouver

Dale M, O'Keefe S, Sebald A, Stepney S, Trefzer MA. Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics. In UCNC 2021: Unconventional Computation and Natural Computation. Vol. 12984. Springer. 2021. p. 19. (LNTCS; 1). https://doi.org/10.1007/978-3-030-87993-8_2

Author

Dale, Matthew ; O'Keefe, Simon ; Sebald, Angelika ; Stepney, Susan ; Trefzer, Martin Albrecht. / Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics. UCNC 2021: Unconventional Computation and Natural Computation. Vol. 12984 Springer, 2021. pp. 19 (LNTCS; 1).

Bibtex - Download

@inproceedings{c6673a6cd33d4b88b878de2ee372a114,
title = "Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics",
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.",
keywords = "magnetic thin films, magnetic materials, unconventional computing, reservoir computing, in-materio computing, substrate geometry",
author = "Matthew Dale and Simon O'Keefe and Angelika Sebald and Susan Stepney and Trefzer, {Martin Albrecht}",
year = "2021",
month = oct,
day = "11",
doi = "10.1007/978-3-030-87993-8_2",
language = "English",
isbn = "978-3-030-87992-1",
volume = "12984",
series = "LNTCS",
publisher = "Springer",
number = "1",
pages = "19",
booktitle = "UCNC 2021: Unconventional Computation and Natural Computation",
note = "Unconventional Computation and Natural Computation 2021<br/>19th International Conference, UCNC 2021, Espoo, Finland, October 18–22, 2021, UCNC 2021 ; Conference date: 18-10-2021 Through 22-10-2021",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics

AU - Dale, Matthew

AU - O'Keefe, Simon

AU - Sebald, Angelika

AU - Stepney, Susan

AU - Trefzer, Martin Albrecht

N1 - Conference code: 19

PY - 2021/10/11

Y1 - 2021/10/11

N2 - 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.

AB - 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.

KW - magnetic thin films

KW - magnetic materials

KW - unconventional computing

KW - reservoir computing

KW - in-materio computing

KW - substrate geometry

U2 - 10.1007/978-3-030-87993-8_2

DO - 10.1007/978-3-030-87993-8_2

M3 - Conference contribution

SN - 978-3-030-87992-1

VL - 12984

T3 - LNTCS

SP - 19

BT - UCNC 2021: Unconventional Computation and Natural Computation

PB - Springer

T2 - Unconventional Computation and Natural Computation 2021<br/>19th International Conference, UCNC 2021, Espoo, Finland, October 18–22, 2021

Y2 - 18 October 2021 through 22 October 2021

ER -