A low power branch predictor to selectively access the BTB

Sung Woo Chung, Sung Bae Park

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

As the pipeline length increases, the accuracy in a branch prediction gets critical to overall performance. In designing a branch predictor, in addition to accuracy, microarchitects should consider power consumption, especially in embedded processors. In this paper, we propose a low power branch predictor, which is based on the gshare predictor, by accessing the BTB (Branch Target Buffer) only when the prediction from the PHT (Prediction History Table) is taken. To enable this, the PHT is accessed one cycle earlier to prevent the additional delay. As a side effect, two predictions from the PHT are obtained at one access to the PHT, which leads to more power reduction. The proposed branch predictor reduces the power consumption, not requiring any additional storage arrays, not incurring additional delay (except just one MUX delay) and never harming accuracy. The simulation results show that the proposed predictor reduces the power consumption by 43-52%.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsPen-Chung Yew, Jingling Xue
PublisherSpringer Verlag
Pages374-384
Number of pages11
ISBN (Electronic)3540230033, 9783540230038
DOIs
Publication statusPublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3189
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Chung, S. W., & Park, S. B. (2004). A low power branch predictor to selectively access the BTB. In P-C. Yew, & J. Xue (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 374-384). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3189). Springer Verlag. https://doi.org/10.1007/978-3-540-30102-8_32