Reconfigurable Reservoir Computing in a Magnetic Metamaterial

Abstract

In-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.

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Cited by in corpus (5)

• Neuromorphic Overparameterisation and Few-Shot Learning in Multilayer Physical Neural Networks
• Artificial Spin Ice: A Tutorial on Design and Control of Geometry, Microstate, Magnon Dynamics & Neuromorphic Computing
• Physical Neural Networks with Self-Learning Capabilities
• Metrics for spin-based computing
• Reservoir computing by thin film embedded with magnetic impurities