Physical reservoir computing using magnetic skyrmion memristor and spin torque nano-oscillator

Abstract

Spintronic nanodevices have ultrafast nonlinear dynamic and recurrence behaviors on a nanosecond scale that promises to enable a high-performance spintronic reservoir computing (RC) system. Here, two physical RC systems based on one single magnetic skyrmion memristor (MSM) and 24 spin-torque nano-oscillators (STNOs) are numerically modeled to process image classification task and nonlinear dynamic system prediction, respectively. Based on the nonlinear responses of the MSM and STNO with current pulse stimulation, our results demonstrate that the MSM-based RC system exhibits excellent performance on image classification, while the STNO-based RC system does well in solving the complex unknown nonlinear dynamic problems, e.g., a second-order nonlinear dynamic system and NARMA10. Our result and analysis of the current-dependent nonlinear dynamic properties of the MSM and STNO provide the strategy to optimize the experimental parameters in building the better spintronic-based brainlike devices for machine learning based computing.