NGRC on SoC for online learning of dynamical systems

Kurzfassung

Predicting the evolution and control of dynamic systems in real time remains a demanding task for conventional methods that depend on accurate models and intensive continuous optimization, which often breaks under uncertainty, noise or delays. Reservoir Computing (RC) provides a lightweight alternative, needing only small training sets and simple inference. We present a low‑cost, low‑power platform that couples next‑generation RC (NGRC) with continuous online learning to forecast long horizons of the Lorenz attractor. The system runs on a Zynq‑7000 SoC: the FPGA accelerates reservoir inference loop, while the ARM CPU manages data acquisition, weight updates and control. With naïve implementation a latency of 160 ms on CPU side + 0.64 ms on FPGA is achieved. Results demonstrate that NGRC on modest SoC hardware can deliver energy‑efficient, real‑time predictions of chaotic systems, enabling edge deployment in autonomous vehicles, robotics, satellites and high‑energy physics.