Abstract
Reducing energy consumption has become the first priority in designing microprocessors for all market segments including embedded, mobile, and high performance processors. The trend of state-of-the-art branch predictor designs such as a hybrid predictor continues to feature more and larger prediction tables, thereby exacerbating the energy consumption. In this paper, we present two novel profile-guided static prediction techniques— Static Correlation Choice (SCC) prediction and Static Choice (SC) prediction for alleviating the energy consumption without compromising performance. Using our techniques, the hardware choice predictor of a hybrid predictor can be completely eliminated from the processor and replaced with our off-line profiling schemes. Our simulation results show an average 40% power reduction compared to several hybrid predictors. In addition, an average 27% die area can be saved in the branch predictor hardware for other performance features.
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Ekpanyapong, M., Korkmaz, P., Lee, HH.S. (2004). Choice Predictor for Free. In: Yew, PC., Xue, J. (eds) Advances in Computer Systems Architecture. ACSAC 2004. Lecture Notes in Computer Science, vol 3189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30102-8_34
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DOI: https://doi.org/10.1007/978-3-540-30102-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23003-8
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