Time multiplexed single node reservoir computing enabled by transient photon magnon coupling

We present a reservoir computing system that utilizes the transient dynamics of photon-magnon coupling (PMC) for high-accuracy temporal data processing, specifically spoken-digit recognition, while minimizing hardware resources. By time-multiplexing a single physical node into ten virtual nodes, our approach achieves 89% classification accuracy, comparable to conventional systems using hundreds or thousands of nodes. This hardware-efficient design preserves the rich nonlinear dynamics essential for temporal processing while significantly reducing energy consumption. In addition, the inter-node connectivity derived from the transient response of PMC requires that the total sampling duration remains below the PMC decay time, and that the pulse interval be sufficiently small to enable overlapping oscillations for effective time-multiplexed reservoir operation. Our results demonstrate that this transient PMC approach can enable scalable, low-power neuromorphic computing for IoT devices, real-time edge computing, and other resource-constrained environments.