Two-level adaptive training branch prediction
MICRO 24 Proceedings of the 24th annual international symposium on Microarchitecture
Alternative implementations of two-level adaptive branch prediction
ISCA '92 Proceedings of the 19th annual international symposium on Computer architecture
A comprehensive instruction fetch mechanism for a processor supporting speculative execution
MICRO 25 Proceedings of the 25th annual international symposium on Microarchitecture
A Further Comparison of Splitting Rules for Decision-Tree Induction
Machine Learning
Dynamic path-based branch correlation
Proceedings of the 28th annual international symposium on Microarchitecture
Technical note: some properties of splitting criteria
Machine Learning
ISCA '96 Proceedings of the 23rd annual international symposium on Computer architecture
Analysis of branch prediction via data compression
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Value locality and load value prediction
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Efficient incremental induction of decision trees
Machine Learning
Dynamic speculation and synchronization of data dependences
Proceedings of the 24th annual international symposium on Computer architecture
Prefetching using Markov predictors
Proceedings of the 24th annual international symposium on Computer architecture
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
The SimpleScalar tool set, version 2.0
ACM SIGARCH Computer Architecture News
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
An analysis of correlation and predictability: what makes two-level branch predictors work
Proceedings of the 25th annual international symposium on Computer architecture
Branch prediction based on universal data compression algorithms
Proceedings of the 25th annual international symposium on Computer architecture
Dynamic history-length fitting: a third level of adaptivity for branch prediction
Proceedings of the 25th annual international symposium on Computer architecture
Correlated load-address predictors
ISCA '99 Proceedings of the 26th annual international symposium on Computer architecture
Memory sharing predictor: the key to a speculative coherent DSM
ISCA '99 Proceedings of the 26th annual international symposium on Computer architecture
Improving branch predictors by correlating on data values
Proceedings of the 32nd annual ACM/IEEE international symposium on Microarchitecture
Selective, accurate, and timely self-invalidation using last-touch prediction
Proceedings of the 27th annual international symposium on Computer architecture
Machine Learning
Neural methods for dynamic branch prediction
ACM Transactions on Computer Systems (TOCS)
Incremental Induction of Decision Trees
Machine Learning
Online Ensemble Learning: An Empirical Study
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Studying New Ways for Improving Adaptive History Length Branch Predictors
ISHPC '02 Proceedings of the 4th International Symposium on High Performance Computing
Predictive sequential associative cache
HPCA '96 Proceedings of the 2nd IEEE Symposium on High-Performance Computer Architecture
Fast Path-Based Neural Branch Prediction
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Two-level branch prediction using neural networks
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Synthesis and verification
Prophet/Critic Hybrid Branch Prediction
Proceedings of the 31st annual international symposium on Computer architecture
Improving branch prediction by considering affectors and affectees correlations
Transactions on high-performance embedded architectures and compilers III
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Most hardware predictors are table based (e.g. two-level branch predictors) and have exponential size growth in the number of input bits or features (e.g. previous branch outcomes). This growth severely limits the amount of predictive information that such predictors can use. To avoid exponential growth we introduce the idea of "dynamic feature selection" for building hardware predictors that can use a large amount of predictive information. Based on this idea, we design the dynamic decision tree (DDT) predictor, which exhibits only linear size growth in the number of features. Our initial evaluation, in branch prediction, shows that the general-purpose DDT, using only branch-history features, is comparable on average to conventional branch predictors, opening the door to practically using large numbers of additional features.