Gesture recognition with Brownian reservoir computing using geometrically confined skyrmion dynamics
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929263981101056 |
|---|---|
| author | Beneke, Grischa Winkler, Thomas Brian Raab, Klaus Brems, Maarten A. Kammerbauer, Fabian Gerhards, Pascal Knobloch, Klaus Mentink, Johan Kläui, Mathias |
| author_facet | Beneke, Grischa Winkler, Thomas Brian Raab, Klaus Brems, Maarten A. Kammerbauer, Fabian Gerhards, Pascal Knobloch, Klaus Mentink, Johan Kläui, Mathias |
| contents | Physical reservoir computing (RC) is a beyond von-Neumann computing paradigm that harnesses the dynamical properties of a complex physical system (reservoir) to process information efficiently in tasks such as pattern recognition. This hardware-centered approach drastically reduces training efforts and holds potential for significantly reduced energy consumption operation. Magnetic skyrmions, topological, particle-like spin textures, are considered highly promising candidates for reservoir computing systems due to their non-linear interactions and established mechanisms for low power manipulation combined with thermally excited dynamics. So far spin-based reservoir computing has been used for static detection or has been based on intrinsic magnetization dynamics timescales, that require cumbersome rescaling of typically slower real-world data. Here we harness the power of time-multiplexed skyrmion RC by adjusting the intrinsic timescales of the reservoir to the timescales of real-world temporal patterns: we recognize hand gestures recorded with range-doppler radar on a millisecond timescale and feed the data as a time-dependent voltage excitation directly into our device. We observe the temporal evolution of the skyrmion trajectory using read-out at just one position of the reservoir, which allows for scaling onto the nanometer scale. We find that our hardware solution exhibits competitive performance compared with a state-of-the-art energy-intensive software-based neural network. The key advantage of our hardware approach lies in its capacity to seamlessly integrate data from sensors without the need for temporal conversion, thanks to the time-dependent input and tunable intrinsic timescales of the skyrmion dynamics in the reservoir. This feature enables real-time feeding of the reservoir, opening a multitude of applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01877 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gesture recognition with Brownian reservoir computing using geometrically confined skyrmion dynamics Beneke, Grischa Winkler, Thomas Brian Raab, Klaus Brems, Maarten A. Kammerbauer, Fabian Gerhards, Pascal Knobloch, Klaus Mentink, Johan Kläui, Mathias Materials Science Physical reservoir computing (RC) is a beyond von-Neumann computing paradigm that harnesses the dynamical properties of a complex physical system (reservoir) to process information efficiently in tasks such as pattern recognition. This hardware-centered approach drastically reduces training efforts and holds potential for significantly reduced energy consumption operation. Magnetic skyrmions, topological, particle-like spin textures, are considered highly promising candidates for reservoir computing systems due to their non-linear interactions and established mechanisms for low power manipulation combined with thermally excited dynamics. So far spin-based reservoir computing has been used for static detection or has been based on intrinsic magnetization dynamics timescales, that require cumbersome rescaling of typically slower real-world data. Here we harness the power of time-multiplexed skyrmion RC by adjusting the intrinsic timescales of the reservoir to the timescales of real-world temporal patterns: we recognize hand gestures recorded with range-doppler radar on a millisecond timescale and feed the data as a time-dependent voltage excitation directly into our device. We observe the temporal evolution of the skyrmion trajectory using read-out at just one position of the reservoir, which allows for scaling onto the nanometer scale. We find that our hardware solution exhibits competitive performance compared with a state-of-the-art energy-intensive software-based neural network. The key advantage of our hardware approach lies in its capacity to seamlessly integrate data from sensors without the need for temporal conversion, thanks to the time-dependent input and tunable intrinsic timescales of the skyrmion dynamics in the reservoir. This feature enables real-time feeding of the reservoir, opening a multitude of applications. |
| title | Gesture recognition with Brownian reservoir computing using geometrically confined skyrmion dynamics |
| topic | Materials Science |
| url | https://arxiv.org/abs/2403.01877 |