A perceptron-based scaled neural predictor (SNP) was implemented to emphasize the most recent branch histories via the following three approaches: (1) expanding the size of tables that correspond to recent branch histories, (2) scaling the branch histories to increase the weights for the most recent histories but decrease those for the old histories, and (3) expanding most recent branch histories to the whole history path. Furthermore, hash mechanisms, and saturating value for adjusting threshold were tuned to achieve the best prediction accuracy in each case. The resulting extended SNP was tested on well-known floating point and integer benchmarks. Using the SimpleScalar 3.0 simulator, while different features have different impact depending on whether the test is floating point or integer, overall such a well-tuned predictor achieves an improved prediction rate compared to prior approaches.

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In Proceedings of the International Joint Conference on Neural Networks, 2013. IEEE.

@inproceedings{zhou:ijcnn13, title={Extended Scaled Neural Predictor for Improved Branch Prediction}, author={Zihao Zhou and Mayank Kejriwal and Risto Miikkulainen}, booktitle={Proceedings of the International Joint Conference on Neural Networks}, publisher={IEEE}, url="http://nn.cs.utexas.edu/?zhou:ijcnn13", year={2013} }