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
Marković, D., Mizrahi, A., Querlioz, D. & Grollier, J. Physics for neuromorphic computing. Nat. Rev. Phys. 2, 499–510 (2020). This review article gives an overview of neuromorphic computing, including reservoir computing.
Jaeger, H. The “Echo State” Approach to Analysing and Training Recurrent Neural Networks — With an Erratum Note GMD Technical Report 148 (German National Research Centre for Information Technology, 2001). This paper introduces the echo-state network, which provides one of the foundations of reservoir computing.
Maass, W., Natschläger, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002). This paper proposes liquid-state machines, which are one of the foundations of reservoir computing.
Tanaka, G. et al. Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019). This review article presents an overview of different physical systems as reservoirs.
Lee, O. et al. Tunable gigahertz dynamics of low-temperature skyrmion lattice in a chiral magnet. J. Phys. Condens. Matter 34, 095801 (2021). This paper outlines protocols for nucleating metastable skyrmions in Cu2OSeO3 by field cycling.
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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
