USING EMBEDDED PROCESSORS IN HARDWARE MODELS OF ARTIFICIAL NEURAL NETWORKS

Abstract

Artificial Neural Networks are applied for solving a wide variety of problems in several areas such as: robotics, image processing, and pattern recognition. Many applications demand a high computing power and the traditional software implementation are not sufficient. Hardware implementations of neural network algorithms are very interesting due their high performance. In this paper, an implementation that joins the software flexibility with the excellent hardware performance has been performed through the use of reconfigurable computing and embedded processors technologies. Keywords Neural Networks, MLP, FPGA, Reconfigurable Computing, Embedded Processors