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
Highly accurate data value prediction using hybrid predictors
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Confidence estimation for speculation control
Proceedings of the 25th annual international symposium on Computer architecture
ISCA '99 Proceedings of the 26th annual international symposium on Computer architecture
Efficacy and Performance Impact of Value Prediction
PACT '98 Proceedings of the 1998 International Conference on Parallel Architectures and Compilation Techniques
Differential FCM: Increasing Value Prediction Accuracy by Improving Table Usage Efficiency
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
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Value prediction is used for overcoming the performance barrier of instruction-level parallelism imposed by data dependencies. Correct predictions allow dependent instructions to be executed earlier. On the other hand mispredictions affect the performance due to a penalty for undoing the speculation meanwhile consuming processor resources that can be used better by non-speculative instructions. A confidence mechanism performs speculation control by limiting the predictions to those that are likely to be correct.When designing a value predictor, hashing functions are useful for compactly representing prediction information but suffer from collisions or hash-aliasing. This hash-aliasing turns out to account for many mispredictions. Our new confidence mechanism has its origin in detecting these aliasing cases through a second, independent, hashing function. Several mispredictions can be avoided by not using predictions suffering from hash-aliasing.Using simulations we show a significant improvement in confidence estimation over known confidence mechanisms, whereas no additional hardware is needed. The combination of independent hashing with saturating counters performs better than pattern recognition, the best confidence mechanism in literature, and it does not need profiling.