Design and Implementation of a Neural Network in an FPGA for Pattern Recovery

Abstract

This research work shows the design and implementation of a Hopfield Artificial Neural Network (ANN) for pattern recovery, starting from noisy patterns, and based on highly complex programmable devices such as Field-Programmable Gate Arrays (FPGAs). Two artificial neural networks were designed, one basic on a FLEX10K FPGA from Altera, and the advanced one on a Cyclone II FPGA from Altera, making use of Electronic Design Automation (EDA) software tools and Hardware Description Language (HDL).

Aurelio Morales Villanueva

Professor of Reconfigurable Computing

My research interests include reconfigurable computing, computer architecture and embedded systems.