A traditional physical-reservoir device has limited flexibility and cannot perform well across a range of computing tasks, owing to the fixed reservoir properties of the physical system. However, by exploiting the rich magnetic phase spaces of a single chiral magnet, reservoir properties can be reconfigured. This control enables on-demand optimization of computational performance across diverse machine-learning tasks.
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References
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This is a summary of: Lee, O. et al. Task-adaptive physical reservoir computing. Nat. Mater. https://doi.org/10.1038/s41563-023-01698-8 (2023).
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Physical reservoir computers that can adapt to perform different tasks. Nat. Mater. 23, 41–42 (2024). https://doi.org/10.1038/s41563-023-01708-9
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DOI: https://doi.org/10.1038/s41563-023-01708-9
