Audio Classification with Skyrmion Reservoirs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866913924907008000 |
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| author | Msiska, Robin Love, Jake Mulkers, Jeroen Leliaert, Jonathan Everschor-Sitte, Karin |
| author_facet | Msiska, Robin Love, Jake Mulkers, Jeroen Leliaert, Jonathan Everschor-Sitte, Karin |
| contents | Physical reservoir computing is a computational paradigm that enables spatio-temporal pattern recognition to be performed directly in matter. The use of physical matter leads the way towards energy-efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. We propose a high performance "skyrmion mixture reservoir" that implements the reservoir computing model with multi-dimensional inputs. We show that our implementation solves spoken digit classification tasks at the nanosecond timescale, with an overall model accuracy of 97.4% and a less that 1% word error rate; the best performance ever reported for in-materio reservoir computers. Due to the quality of the results and the low power properties of magnetic texture reservoirs, we argue that skyrmion fabrics are a compelling candidate for reservoir computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_13946 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Audio Classification with Skyrmion Reservoirs Msiska, Robin Love, Jake Mulkers, Jeroen Leliaert, Jonathan Everschor-Sitte, Karin Mesoscale and Nanoscale Physics Physical reservoir computing is a computational paradigm that enables spatio-temporal pattern recognition to be performed directly in matter. The use of physical matter leads the way towards energy-efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. We propose a high performance "skyrmion mixture reservoir" that implements the reservoir computing model with multi-dimensional inputs. We show that our implementation solves spoken digit classification tasks at the nanosecond timescale, with an overall model accuracy of 97.4% and a less that 1% word error rate; the best performance ever reported for in-materio reservoir computers. Due to the quality of the results and the low power properties of magnetic texture reservoirs, we argue that skyrmion fabrics are a compelling candidate for reservoir computing. |
| title | Audio Classification with Skyrmion Reservoirs |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2209.13946 |