Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Value locality and load value prediction
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Exceeding the dataflow limit via value prediction
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
The predictability of data values
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Highly accurate data value prediction using hybrid predictors
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Predictive techniques for aggressive load speculation
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Neural methods for dynamic branch prediction
ACM Transactions on Computer Systems (TOCS)
Using Dataflow Based Context for Accurate Value Prediction
Proceedings of the 2001 International Conference on Parallel Architectures and Compilation Techniques
Global Context-Based Value Prediction
HPCA '99 Proceedings of the 5th International Symposium on High Performance Computer Architecture
Detecting global stride locality in value streams
Proceedings of the 30th annual international symposium on Computer architecture
| Hi-index | 0.00 |
Data dependencies between instructions have traditionally limited the ability of processors to execute instructions in parallel. Data value predictors are used to overcome these dependencies by guessing the outcomes of instructions. Because mispredictions can result in a significant performance decrease, most data value predictors include a confidence estimator that indicates whether a prediction should be used. This paper presents a global approach to confidence estimation in which the prediction accuracy of previous instructions is used to estimate the confidence of the current prediction. Perceptrons are used to identify which past instructions affect the accuracy of a prediction and to decide whether the prediction is likely to be correct. Simulation studies compare this global confidence estimator to the more conventional local confidence estimator. Results show that predictors using this global confidence estimator tend to predict significantly more instructions and incur fewer mispredictions than predictors using existing local confidence estimation approaches.