A Reconfigurable Computing Architecture for Implementing Artificial Neural Networks on Fpga

A RECONFIGURABLE COMPUTING ARCHITECTURE FOR IMPLEMENTING ARTIFICIAL NEURAL NETWORKS ON FPGA Kristian Nichols University of Guelph, 2003 Advisor: Professor Medhat Moussa Professor Shawki Areibi Artificial Neural Networks (ANNs), and the backpropagation algorithm in particular, is a form of artificial intelligence that has traditionally suffered from slow training and lack of clear methodology to determine network topology before training starts. Past researchers have used reconfigurable computing as one means of accelerating ANN testing. The goal of this thesis was to learn how recent improvements in the tools and methodologies used in reconfigurable computing have helped advanced the field, and thus, strengthened its applicability towards accelerating ANNs. A new FPGA-based ANN architecture, called RTR-MANN, was created to demonstrate the performance enhancements gained from using current-generation tools and methodologies. RTR-MANN was shown to have an order of magnitude more scalability and functional density compared to older-generation FPGAbased ANN architectures. In addition, use of a new system design methodology (via Highlevel Language) led to a more intuitive verification / validation phase, which was an order of magnitude faster compared traditional HDL simulators.