Time Delay Reservoir Computing in a Single Physical Node Enabled by Transient Photon Magnon Coupling

We present a reservoir computing system that utilizes the transient dynamics of photon-magnon coupling (PMC) to achieve high-accuracy temporal data processing specifically spoken digit recognition while minimizing hardware resources. By employing time-multiplexing a single physical node to create an addition 9 virtual nodes, our approach attains an 89% classification accuracy, comparable to conventional systems with 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 chosen delay (𝜏) remains below the overall decay time (𝑇), and that the pulse interval (𝛿) be sufficiently small to enable overlapping oscillations for effective time-delay-based 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.