Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetization dynamics

Devices based on arrays of interconnected magnetic nano-rings with emergent

magnetization dynamics have recently been proposed for use in reservoir

computing applications, but for them to be computationally useful it must be

possible to optimise their dynamical responses. Here, we use a phenomenological

model to demonstrate that such reservoirs can be optimised for classification

tasks by tuning hyperparameters that control the scaling and input rate of data

into the system using rotating magnetic fields. We use task-independent metrics

to assess the rings' computational capabilities at each set of these

hyperparameters and show how these metrics correlate directly to performance in

spoken and written digit recognition tasks. We then show that these metrics,

and performance in tasks, can be further improved by expanding the reservoir's

output to include multiple, concurrent measures of the ring arrays magnetic

states.