FPGA Implementation of Next-Generation Reservoir Computing for predicting dynamical systems

Kurzfassung

Reservoir Computing (RC) is a new paradigm in Machine Learning, alternative to Neural Networks on predicting dynamical systems, offering advantages in efficiency and computational simplicity. These characteristics make RC particularly well-suited for implementation on resource-constrained hardware such as FPGAs, enabling low-power, real-time edge computing. Next-Generation Reservoir Computing (NG-RC) further enhances this approach by significantly reducing the number of required parameters compared to conventional RC, making FPGA implementations even more efficient. In this work, we present an FPGA-based implementation of NG-RC for predicting the Lorenz attractor, demonstrating its effectiveness in modeling chaotic systems with minimal computational overhead.