Dynamic Branch Prediction Using Neural Networks

Dynamic 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 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 f o r 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 (L VQ) Network and a Backpropagation Network. We demonstrate that a neural predictor can achieve misprediction rates comparable to conventional Two-level Adaptive Predictors and suggest that neural predictors merit further investigation.