A Simple yet Accurate Neural Branch Predictor

S.P. Hunt, C. Egan, and A. Shafarenko (UK)


Neural Networks, Computing, Engineering, Optimization


In this paper, we examine the application of simple neural processing elements to the problem of dynamic branch prediction in high-performance processors. A single neural network model is considered: the Perceptron. We demonstrate that a predictor based on the Perceptron can achieve a prediction accuracy in excess of that given by conventional Two-level Adaptive Predictors and suggest that neural predictors merit further investigation.

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