Implementation of an Integrated Artificial Neural Network Trained with Back Propagation Algorithm

Abstract

Artificial Neural Network (ANN) is a mathematical model that is inspired by the structureand/or functional aspects of biological neural networks.A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. This thesis is an effort towards the implementation of an integrated ANN trained with backpropagation algorithm. This work discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model. It presents ASIC (semi-custom)and FPGA implementation of the network for solving the XOR problem using Fixedpoint format (FXP) for representing real numbers. Implementation of squashing function has also been achieved using appropriate approximation techniques. The thesis concludes with a comparison of results obtained for ASIC and FPGA.