Spintronic reservoir computing with interpretable nonlinearity

Reservoir computing (RC) is a computational framework that utilizes a nonlinear, memory-based kernel to map time series inputs into a high-dimensional space for information processing. Most hardware-based RC systems treat their physical reservoirs as black boxes, with the roles of their complex nonlinear dynamics empirically understood and adjusted by tuning physical conditions. However, theoretically understanding the reservoir's nonlinearity in relation to its computational capabilities is crucial for ensuring consistent performance and adaptive system design. In this paper, we propose a magnetization-saturation-based physical reservoir with interpretable nonlinearity, where spin dynamics follows a hyperbolic tangent function and its nonlinear scaling is analytically governed by the damping constant of the magnetic material. Our spin reservoir consists of a magnetic thin film segmented into multiple subregions with different damping constants. This design enables not only nonlinearity diversity through a variety of nonlinear scaling but also short-term memory through hysteresis interactions of neighboring spins. We numerically validate our spin RC by demonstrating its high performance in nonlinear waveform transformation and temporal exclusive-OR (XOR) tasks, which aligns with our theoretical understanding of the system. We expect this work to contribute to the advancement of reliable and configurable spintronic RC systems.