Video recognition by physical reservoir computing in magnetic materials

Fuente: arXiv
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Autores principales: Kobayashi, Kaito, Motome, Yukitoshi
Formato: Preprint
Publicado: 2023
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author Kobayashi, Kaito
Motome, Yukitoshi
author_facet Kobayashi, Kaito
Motome, Yukitoshi
contents Nonlinear spin dynamics in magnetic materials offers a promising avenue for implementing physical reservoir computing, one of the most accomplished brain-inspired frameworks for information processing. In this study, we investigate the practical utility of magnetic physical reservoirs by assessing their performance in a video recognition task. Leveraging a recently developed spatiotemporal parallelization scheme, our reservoir achieves accurate classifications of previously provided images. Our findings pave the way for the development of visual sensors based on the magnetic physical reservoir computing.
format Preprint
id arxiv_https___arxiv_org_abs_2310_06497
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Video recognition by physical reservoir computing in magnetic materials
Kobayashi, Kaito
Motome, Yukitoshi
Mesoscale and Nanoscale Physics
Nonlinear spin dynamics in magnetic materials offers a promising avenue for implementing physical reservoir computing, one of the most accomplished brain-inspired frameworks for information processing. In this study, we investigate the practical utility of magnetic physical reservoirs by assessing their performance in a video recognition task. Leveraging a recently developed spatiotemporal parallelization scheme, our reservoir achieves accurate classifications of previously provided images. Our findings pave the way for the development of visual sensors based on the magnetic physical reservoir computing.
title Video recognition by physical reservoir computing in magnetic materials
topic Mesoscale and Nanoscale Physics
url https://arxiv.org/abs/2310.06497