# Fast physical reservoir computing, achieved with nonlinear interfered spin waves

https://mdr.nims.go.jp/datasets/f70bbe67-9654-4e0d-ac06-736dab9756aa

## File

- [Namiki_2024_Neuromorph._Comput._Eng._4_024015-2.pdf](https://mdr.nims.go.jp/filesets/38e187c3-ecfe-4298-9caa-c2200a39c236/download) ([Detail](https://mdr.nims.go.jp/filesets/38e187c3-ecfe-4298-9caa-c2200a39c236.md))

## Id

f70bbe67-9654-4e0d-ac06-736dab9756aa

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-08-02T10:14:51.935823Z

## Updated at

2024-08-05T03:30:22.826535Z

## Published at

2024-08-05T03:30:22.894739Z

## Doi



## First published url

https://doi.org/10.1088/2634-4386/ad561a

## Date published

2024-06-01

## Recorded date published

2024-6-1

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Fast physical reservoir computing, achieved with nonlinear interfered spin
    waves
  title_type: original
  lang: en

## Description

- description: Reservoir computing is a promising approach to implementing high-performance
    artificial intelligence that can process input data at lower computational costs
    than conventional artificial neural networks. Although reservoir computing enables
    real-time processing of input time-series data on artificial intelligence mounted
    on terminal devices, few physical devices are capable of high-speed operation
    for real-time processing. In this study, we introduce spin wave interference with
    a stepped input method to reduce the operating time of the physical reservoir,
    and second-order nonlinear equation task and second-order nonlinear autoregressive
    mean averaging, which are well-known benchmark tasks, were carried out to evaluate
    the operating speed and prediction accuracy of said physical reservoir. The demonstrated
    reservoir device operates at the shortest operating time of 13 ms/5000-time steps,
    compared to other compact reservoir devices, even though its performance is higher
    than or comparable to such physical reservoirs. This study is a stepping stone
    toward realizing an artificial intelligence device capable of real-time processing
    on terminal devices.
  description_type: abstract
  lang: und

## Creator

- name: Wataru Namiki
  role: author
  orcid: https://orcid.org/0000-0003-4053-7366
  organization: National Institute for Materials Science
- name: Daiki Nishioka
  role: author
  orcid: https://orcid.org/0000-0002-3369-7700
  organization: National Institute for Materials Science
- name: Takashi Tsuchiya
  role: author
  orcid: https://orcid.org/0000-0002-6950-6160
  organization: National Institute for Materials Science
- name: Kazuya Terabe
  role: author
  orcid: https://orcid.org/0000-0003-3988-3456
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: IOP Publishing

## Managing organization



## Keyword

- subject: Spin wave inteference
  schema: not_defined
- subject: Neuromorphic computing
  schema: not_defined
- subject: Reservoir computing
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Neuromorphic Computing and Engineering
  issn: '26344386'
  volume: '4'
  issue: '2'
  article_number: '024015'

## Conference



## Related item



## Funding

- identifier: JP21J21982
  funder_name: Japan Society for the Promotion of Science
- identifier: JPJ004596
  funder_name: Innovative Science and Technology Initiative for Security Grant
- identifier: JPMXP1223NM5072
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- funder_name: JSPS
- funder_name: National Institute for Materials Science

## Instrument



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## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



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## Fileset

- id: 38e187c3-ecfe-4298-9caa-c2200a39c236
  filename: Namiki_2024_Neuromorph._Comput._Eng._4_024015-2.pdf
  content_type: application/pdf
  size: 3200517
  md5: dd099e6e4807b7823667d4c5b1eacea4

## Thumbnail

fileset_id: 38e187c3-ecfe-4298-9caa-c2200a39c236
filename: Namiki_2024_Neuromorph._Comput._Eng._4_024015-2.pdf