Abstract.
Dynnamic branch prediction in high-performance processors is a specific instance of a general Time Series Prediction problem that occurs in many areas of science. In contrast, most current branch prediction research focuses on Two-Level Adaptive Branch Prediction techniques, a very specific solution to the branch prediction problem. An alternative approach is to look to other application areas and fields for novel solutions to the problem. In this paper we examine the application of neural networks to dynamic branch prediction. Two neural networks are considered, a Learning Vector Quantisation (LVQ) Network and a Multi-Layer Perceptron Network accomplished by the Backpropagation learning algorithm. We demonstrate that a neural predictor can achieve prediction rates better than conventional two-level adaptive predictors and therefore suggest that neural predictors are a suitable vehicle for future branch prediction research.