Optimization of the performance of ANN using VHDL

In this paper, we proposed the design method of artificial neural networks using VHDL and implement in FPGA. VHDL is a programming language that has been designed and optimized for describing the behavior of digital systems. Back propagation algorithm for the design of a neuron is described. Back propagation is popular training algorithms for multilayer perceptrons. Over the last years many improvement strategies have been developed to speed up back propagation. It is very difficult to compare these different techniques, because most of them have been tested on very special data sets. Many “optimized” algorithms failed in training the considered task. Still back propagation is superior. The sigmoid nonlinear activation function is also used [1]. The neuron is then used in the design and implementation of a neural network using FPGA [12]. The simulation is done with Xilinx ISE 9.1i software. The neuron is then used in a multilayer neural network.The purpose of this work is to suggest and analyze several neuron implementations, show a way for the integration and control of the neurons within a neural network, and describe a way to implement a simple feed-forward neural network trained by BP algorithm using Xilinx software and implement in FPGA.