Application of Neural Network to Improve Dynamic Branch Prediction of Superscalar Microprocessors

This research shows that using an Artificial Neural Network as the hardware branch predictor of a superscalar microprocessor leads to performance as good as standard branch predictors for comparable chip area. The results were obtained running several Spec95 benchmarks on an augmented version of the simple-scalar architecture simulator. The approach taken in this research is an attempt to use Neural Networks to improve the design of hardware branch predictors. It points to a combination of static and dynamic techniques using artificial intelligence. The prediction rates achieved by the holistic-non-adaptive Neura