Abstract
AbstractIn-materia reservoir computing (RC) leverages the intrinsic physical responses of functional materials to perform complex computational tasks. Magnetic metamaterials are exciting candidates for RC due to their huge state space, nonlinear emergent dynamics, and non-volatile memory. However, to be suitable for a broad range of tasks, the material system is required to exhibit a broad range of properties, and isolating these behaviours experimentally can often prove difficult. By using an electrically accessible device consisting of an array of interconnected magnetic nanorings- a system shown to exhibit complex emergent dynamics- here we show how reconfiguring the reservoir architecture allows exploitation of different aspects the system’s dynamical behaviours. This is evidenced through state-of-the-art performance in diverse benchmark tasks with very different computational requirements, highlighting the additional computational configurability that can be obtained by altering the input/output architecture around the material system.
Funder
RCUK | Engineering and Physical Sciences Research Council
EC | Horizon 2020 Framework Programme
Leverhulme Trust
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy
Reference47 articles.
1. Zou, X., Xu, S., Chen, X., Liang, Y. & Han, Y. Breaking the von Neumann bottleneck: architecture-level processing-in-memory technology. Sci. China Inf. Sci. 64, 160404:1–160404:10 (2021).
2. Jaeger, H. The “Echo State” Approach To Analysing And Training Recurrent Neural Networks- With An Erratum Note. GMD Technical Report (2001).
3. Lukoševičius, M., Jaeger, H. & Schrauwen, B. Reservoir computing trends. KI - Kunstliche Intell. 26, 365–371 (2012).
4. Lukoševičius, M. & Jaeger, H. Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009).
5. Paquot, Y. et al. Optoelectronic reservoir computing. Sci. Rep. 2, 287 (2012).
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献