Towards an Energy Efficient Branch Prediction Scheme Using Profiling, Adaptive Bias Measurement and Delay Region Scheduling.
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Author
Hicks, M.
Egan, C.
Christianson, B.
Quick, P.
Attention
2299/1005
Abstract
Dynamic branch predictors account for between
10% and 40% of a processor’s dynamic power consumption.
This power cost is proportional to the number of accesses made
to that dynamic predictor during a program’s execution. In
this paper we propose the combined use of local delay region
scheduling and profiling with an original adaptive branch bias
measurement. The adaptive branch bias measurement takes note
of the dynamic predictor’s accuracy for a given branch and
decides whether or not to assign a static prediction for that
branch. The static prediction and local delay region scheduling
information is represented as two hint bits in branch instructions.
We show that, with the combined use of these two methods, the
number of dynamic branch predictor accesses/updates can be
reduced by up to 62%. The associated average power saving is
very encouraging; for the example high-performance embedded
architecture n average global processor power saving of 6.22% is achieved.