TY - CHAP
T1 - A low power branch predictor to selectively access the BTB
AU - Chung, Sung Woo
AU - Park, Sung Bae
PY - 2004
Y1 - 2004
N2 - 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%.
AB - 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%.
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U2 - 10.1007/978-3-540-30102-8_32
DO - 10.1007/978-3-540-30102-8_32
M3 - Chapter
AN - SCOPUS:35048840660
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 374
EP - 384
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Yew, Pen-Chung
A2 - Xue, Jingling
PB - Springer Verlag
ER -