文章基本信息

标题：Implementation of the RN Method on FPGA using Xilinx System Generator for Nonlinear System Regression
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作者：Intissar SAYEHI ; Okba TOUALI ; T. Saidani 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2017
卷号：8
期号：6
DOI：10.14569/IJACSA.2017.080619
出版社：Science and Information Society (SAI)
摘要：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 Co-Design 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.
关键词：Machine learning; Reproducing Kernel Hilbert Spaces (RKHS); regularization networks; FPGA; HW/SW Co-simulation; systolic array architecture; PT326; Wiener-Hammerstein benchmark