基于现场可编程门阵列的径向基函数网络的并行定点实现

Parallel fixed point implementation of a radial basis function network in an FPGA.

作者信息

de Souza Alisson C D, Fernandes Marcelo A C

机构信息

Department of Computer Engineering and Automation, Center of Technology, Federal University of Rio Grande do Norte-UFRN, Natal 59078-970, Brazil.

出版信息

Sensors (Basel). 2014 Sep 29;14(10):18223-43. doi: 10.3390/s141018223.

DOI:10.3390/s141018223

PMID:25268918

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4239954/

Abstract

This paper proposes a parallel fixed point radial basis function (RBF) artificial neural network (ANN), implemented in a field programmable gate array (FPGA) trained online with a least mean square (LMS) algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx), with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.

摘要

本文提出了一种并行定点径向基函数（RBF）人工神经网络（ANN），该网络在现场可编程门阵列（FPGA）中实现，并采用最小均方（LMS）算法进行在线训练。针对各种定点格式分析了处理时间和占用面积。在硬件实现中还分析了使用异或门进行非线性分类时ANN响应的精度问题以及使用正弦函数进行插值的问题。整个项目是使用系统生成器平台（赛灵思）开发的，目标FPGA为Virtex-6 xc6vcx240t-1ff1156。