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http://dx.doi.org/10.3745/KIPSTA.2002.9A.4.497

2-Level Adaptive Branch Prediction Based on Set-Associative Cache  

Shim, Won (서울산업대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartA / v.9A, no.4, 2002 , pp. 497-502 More about this Journal
Abstract
Conditional branches can severely limit the performance of instruction level parallelism by causing branch penalties. 2-level adaptive branch predictors were developed to get accurate branch prediction in high performance superscalar processors. Although 2 level adaptive branch predictors achieve very high prediction accuracy, they tend to be very costly. In this paper, set-associative cached correlated 2-level branch predictors are proposed to overcome the cost problem in conventional 2-level adaptive branch predictors. According to simulation results, cached correlated predictors deliver higher prediction accuracy than conventional predictors at a significantly lower cost. The best misprediction rates of global and local cached correlated predictors using set-associative caches are 5.99% and 6.28% respectively. They achieve 54% and 17% improvements over those of the conventional 2-level adaptive branch predictors.
Keywords
Branch Prediction; Dynamic Branch Prediction; 2-Level Adaptive Branch Prediction; Cached Correlated Branch Prediction;
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