Two-level adaptive training branch prediction
MICRO 24 Proceedings of the 24th annual international symposium on Microarchitecture
ISCA '96 Proceedings of the 23rd annual international symposium on Computer architecture
Evidence-based static branch prediction using machine learning
ACM Transactions on Programming Languages and Systems (TOPLAS)
Assigning confidence to conditional branch predictions
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
The agree predictor: a mechanism for reducing negative branch history interference
Proceedings of the 24th annual international symposium on Computer architecture
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Confidence estimation for speculation control
Proceedings of the 25th annual international symposium on Computer architecture
Pipeline gating: speculation control for energy reduction
Proceedings of the 25th annual international symposium on Computer architecture
The YAGS branch prediction scheme
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Selective branch inversion: confidence estimation for branch predictors
International Journal of Parallel Programming - parallel architectures and compilation techniques, part II
Neural methods for dynamic branch prediction
ACM Transactions on Computer Systems (TOCS)
Dynamic Branch Prediction with Perceptrons
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
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In 2001, Jiménez and Lin [Dynamic branch prediction with perceptrons, Proceedings of the 7th International Symposium on High Performance Computer Architecture, 2001, pp. 197-206] introduced the perceptron branch predictor, the first dynamic branch predictor to successfully use neural networks. This simple neural network achieves higher accuracies (95% at a 4 KiB hardware budget) compared to other existing branch predictors and provides a free confidence level. In this paper, we first gain insight into this inherent confidence mechanism of the perceptron predictor and explain why (additional) counter based confidence strategies can complement it. Second, we evaluate several composite confidence estimation strategies and compare them to the described technique by Jiménez and Lin [Composite confidence estimators for enhanced speculation control, Tech. rep., Department of Computer Sciences, The University of Texas at Austin, 2002]. We conclude that our overruling AND-combination of perceptron confidence and resetting counter mechanism outperforms the previously proposed confidence scheme.