دورية
Task-adaptive physical reservoir computing
| العنوان: | Task-adaptive physical reservoir computing |
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| المؤلفون: | Lee, Oscar, Wei, Tianyi, Stenning, Kilian D., Gartside, Jack C., Prestwood, Dan, Seki, Shinichiro, Aqeel, Aisha, Karube, Kosuke, Kanazawa, Naoya, Taguchi, Yasujiro, Back, Christian, Tokura, Yoshinori, Branford, Will R., Kurebayashi, Hidekazu |
| المصدر: | Nature Materials; 20230101, Issue: Preprints p1-9, 9p |
| مستخلص: | Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in ‘physical’ reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a ‘task-adaptive’ approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3(and FeGe). |
| قاعدة البيانات: | Supplemental Index |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 14761122 14764660 |
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| DOI: | 10.1038/s41563-023-01698-8 |