Branch history table prediction of moving target branches due to subroutine returns
ISCA '91 Proceedings of the 18th annual international symposium on Computer architecture
Two-level adaptive training branch prediction
MICRO 24 Proceedings of the 24th annual international symposium on Microarchitecture
Reducing indirect function call overhead in C++ programs
POPL '94 Proceedings of the 21st ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Next cache line and set prediction
ISCA '95 Proceedings of the 22nd annual international symposium on Computer architecture
Target prediction for indirect jumps
Proceedings of the 24th annual international symposium on Computer architecture
Path-based next trace prediction
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Dynamic history-length fitting: a third level of adaptivity for branch prediction
Proceedings of the 25th annual international symposium on Computer architecture
The cascaded predictor: economical and adaptive branch target prediction
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Predicting indirect branches via data compression
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Skewed Associativity Improves Program Performance and Enhances Predictability
IEEE Transactions on Computers
Wide and efficient trace prediction using the local trace predictor
Proceedings of the 20th annual international conference on Supercomputing
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Accurate indirect jump prediction is critical for some applications. Proposed methods are not efficient in terms of chip area. Our proposal evaluates a mechanism called target encoding that provides a better ratio between prediction accuracy and the amount of bits devoted to the predictor. The idea is to encode full target addresses into shorter target identifiers, so that more entries can be stored with a fixed memory budget, and longer branch histories can be used to increase prediction accuracy. With a fixed area budget, the increase in accuracy for the proposed scheme ranges from 10% to up to 90%. On the other hand, the new scheme provides the same accuracy while reducing predictor size by between 35% and 70%.