The predictability of data values
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Improving branch predictors by correlating on data values
Proceedings of the 32nd annual ACM/IEEE international symposium on Microarchitecture
Neural methods for dynamic branch prediction
ACM Transactions on Computer Systems (TOCS)
Control-Flow Speculation through Value Prediction
IEEE Transactions on Computers
Dynamic Branch Prediction with Perceptrons
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
An alternative to branch prediction: pre-computed branches
ACM SIGARCH Computer Architecture News
Understanding prediction limits through unbiased branches
ACSAC'06 Proceedings of the 11th Asia-Pacific conference on Advances in Computer Systems Architecture
Exploiting selective instruction reuse and value prediction in a superscalar architecture
Journal of Systems Architecture: the EUROMICRO Journal
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The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches in terms of prediction accuracy on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Since our focus is on the impact that unbiased branches have on processor performance, timing issues and hardware costs are out of scope of this investigation. Our simulation results, with the SPEC2000 integer benchmark suite, are interesting even though they show that unbiased branches still restrict the ceiling of branch prediction and therefore accurately predicting unbiased branches remains an open problem.