Implementation of the RN Method on FPGA using Xilinx System Generator for Nonlinear System Regression

In this paper, we propose a new approach aiming to ameliorate the performances of the regularization networks (RN) method and speed up its computation time. A considerable rapidity in totaling calculation time and high performance were accomplished through conveying difficult calculation charges to FPGA. Using Xilinx System Generator, a successful HW/SW CoDesign was constructed to accelerate the Gramian matrix computation. Experimental results involving two real data sets of Wiener-Hammerstein benchmark with process noise prove the efficiency of the approach. The implementation results demonstrate the efficiency of the heterogeneous architecture, presenting a speed-up factor of 40-50 orders of time, comparing to the CPU simulation. Keywords—Machine learning; Reproducing Kernel Hilbert Spaces (RKHS); regularization networks; FPGA; HW/SW Cosimulation; systolic array architecture; PT326; WienerHammerstein benchmark