Implementation of Efficient Multilayer Perceptron ANN Neurons on Field Programmable Gate Array Chip

Abstract

Artificial Neural Network is widely used to learn data from systems for different types of applications. The capability of different types of Integrated Circuit (IC) based ANN structures also depends on the hardware backbone used for their implementation. In this work, Field Programmable Gate Array (FPGA) based Multilayer Perceptron Artificial Neural Network (MLP-ANN) neuron is developed. Experiments were carried out to demonstrate the hardware realization of the artificial neuron using FPGA. Two different activation functions (i.e. tan-sigmoid and log-sigmoid) were tested for the implementation of the proposed neuron. Simulation result shows that tan-sigmoid with a high index (i.e. k >= 40) is a better choice of sigmoid activation function for the harware implemetation of a MLP-ANN neuron