0000000000451649

AUTHOR

Colin Egan

showing 3 related works from this author

Understanding Prediction Limits Through Unbiased Branches

2006

The majority of currently available branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches which are difficult-to-predict. In this paper, we quantify and evaluate the impact of unbiased branches and show that any gain in prediction accuracy is proportional to the frequency of unbiased branches. By using the SPECcpu2000 integer benchmarks we show that there are a significant proportion of unbiased branches which severely impact on prediction accuracy (averaging between 6% and 24% depending on the prediction context used).

Ramification (botany)StatisticsEconometricsContext (language use)Unbiased EstimationBest linear unbiased predictionBranch predictorMathematicsInteger (computer science)
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Two-level branch prediction using neural networks

2003

Dynamic branch prediction in high-performance processors is a specific instance of a general time series prediction problem that occurs in many areas of science. Most branch prediction research focuses on two-level adaptive branch prediction techniques, a very specific solution to the branch prediction problem. An alternative approach is to look to other application areas and fields for novel solutions to the problem. In this paper, we examine the application of neural networks to dynamic branch prediction. We retain the first level history register of conventional two-level predictors and replace the second level PHT with a neural network. Two neural networks are considered: a learning vec…

Artificial neural networkbusiness.industryTime delay neural networkComputer scienceVector quantizationLearning vector quantisationBranch predictorMachine learningcomputer.software_genreBackpropagationApplication areasHardware and ArchitectureArtificial intelligenceHardware_CONTROLSTRUCTURESANDMICROPROGRAMMINGTime seriesbusinesscomputerSoftwareJournal of Systems Architecture
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Unbiased Branches: An Open Problem

2007

The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches in terms of prediction accuracy on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Since our focus is on the impact that unbiased branches have on processor performance, timing issues and hardware costs are out of scope of this investigation. Our simulation results, with the SPEC2000 in…

Mathematical optimizationMarkov chainComputer sciencebusiness.industryOpen problemPrediction by partial matchingBest linear unbiased predictionMachine learningcomputer.software_genreBranch predictorBenchmark (computing)Range (statistics)Artificial intelligenceHardware_CONTROLSTRUCTURESANDMICROPROGRAMMINGbusinesscomputerInteger (computer science)
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