Fast Path-Based Neural Branch Prediction
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Power-Aware Branch Prediction: Characterization and Design
IEEE Transactions on Computers
Alloyed branch history: combining global and local branch history for robust performance
International Journal of Parallel Programming
Improved latency and accuracy for neural branch prediction
ACM Transactions on Computer Systems (TOCS)
Clustered indexing for branch predictors
Microprocessors & Microsystems
Thrifty BTB: A comprehensive solution for dynamic power reduction in branch target buffers
Microprocessors & Microsystems
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The need for accurate conditional-branch prediction is well known: mispredictions waste large numbers of cycles, inhibit out-of-order execution, and waste power on mis-speculated computation. Prior work on branch-predictor organization has focused mainly on how to reduce conflicts in the branch-predictor structures, while relatively little work has explored other causes of mis-predictions. Some prior work has identified other categories of mispredictions, but this paper organizes these categories into a broad taxonomy of misprediction types. Using the taxonomy, this paper goes on to show that other categories-especially wrong-history mispredictions-are often more important than conflicts. This is true even if just a very simple conflict-reduction technique is used. Based on these observations, this paper proposes alloying local and global history together in a two-level branch predictor structure. This simple technique, a generalization of the bi-mode predictor, attacks wrong-history mispredictions by making both global and local history simultaneously available. Unlike hybrid prediction, however, alloying gives robust performance for branch-predictor hardware budgets ranging from very large to very small. Finally, this paper shows that individual branch references can also suffer wrong-history mispredictions as they alternate between using global and local history, a phenomenon that favors dynamic rather than static selection in hybrid predictors.