Low power/area branch prediction using complementary branch predictors

Resit Sendag, Joshua J. Yi, Peng Fei Chuang, David J Lilja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Although high branch prediction accuracy is necessary for high performance, it typically comes at the cost of larger predictor tables and/or more complex prediction algorithms. Unfortunately, large predictor tables and complex algorithms require more chip area and have higher power consumption, which precludes their use in embedded processors. As an alternative to large, complex branch predictors, in this paper, we investigate adding complementary branch predictors (CBP) to embedded processors to reduce their power consumption and/or improve their branch prediction accuracy. A CBP differs from a conventional branch predictor in that it focuses only on frequently mispredicted branches while letting the conventional branch predictor predict the more predictable ones. Our results show that adding a small 16-entry (28 byte) CBP reduces the branch misprediction rate of static, bimodal, and gshare branch predictors by an average of 51.0%, 42.5%, and 39.8%, respectively, across 38 SPEC 2000 and MiBench benchmarks. Furthermore, a 256-entry CBP improves the energy-efficiency of the branch predictor and processor up to 97.8% and 23.6%, respectively. Finally, in addition to being very energy-efficient, a CBP can also improve the processor performance and, due to its simplicity, can be easily added to the pipeline of any processor.

Original languageEnglish (US)
Title of host publicationIPDPS Miami 2008 - Proceedings of the 22nd IEEE International Parallel and Distributed Processing Symposium, Program and CD-ROM
DOIs
StatePublished - Sep 10 2008
EventIPDPS 2008 - 22nd IEEE International Parallel and Distributed Processing Symposium - Miami, FL, United States
Duration: Apr 14 2008Apr 18 2008

Other

OtherIPDPS 2008 - 22nd IEEE International Parallel and Distributed Processing Symposium
CountryUnited States
CityMiami, FL
Period4/14/084/18/08

Fingerprint Dive into the research topics of 'Low power/area branch prediction using complementary branch predictors'. Together they form a unique fingerprint.

Cite this