Optimizing FPGA implementation of Feed-Forward Neural Networks

Abstract

This paper presents an in depth study of FPGA implementation of feed-forward neural networks regarding error reduction as a function of number of bits used for weights representation. Xilinx block parameters influence on resources occupied by network and the maximum working frequency is also studied.