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


The KIPS Transactions:PartA, Vol. 9, No. 4, pp. 497-502, Dec. 2002
10.3745/KIPSTA.2002.9.4.497,   PDF Download:

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.


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Cite this article
[IEEE Style]
W. Shim, "2-Level Adaptive Branch Prediction Based on Set-Associative Cache," The KIPS Transactions:PartA, vol. 9, no. 4, pp. 497-502, 2002. DOI: 10.3745/KIPSTA.2002.9.4.497.

[ACM Style]
Won Shim. 2002. 2-Level Adaptive Branch Prediction Based on Set-Associative Cache. The KIPS Transactions:PartA, 9, 4, (2002), 497-502. DOI: 10.3745/KIPSTA.2002.9.4.497.