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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