FPGA implementation of environment/noise classification using neural networks

Abstract

The purpose of this thesis is to give an insight into the implementation of a system of neural networks, for the tasks of Noise/Environment Modeling, Feature Extraction and Classification of Noise/Environment, on a Field Programmable Gate Array (FPGA). A methodology for creating baseline architecture for a new system of neural networks has been followed, to give worst case estimates. After necessary analysis an estimate of hardware utilization, within a specific FPGA (XC3S250E Spartan 3E Device) and the Time for Computation, for each of the machines used, is given. It also summarizes the Performance-Price Ratio in terms of Time of Computation and Hardware for Logic simplementation, for different degrees of parallelism in the system.