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Conference Papers Year : 2019

A Time-predictable Branch Predictor

Abstract

Long pipelines need good branch predictors to keep the pipeline running. Current branch predictors are optimized for the average case, which might not be a good fit for real-time systems and worst- case execution time analysis. This paper presents a time-predictable branch predictor co-designed with the associated worst-case execution time analysis. The branch predictor uses a fully-associative cache to track branch outcomes and destination addresses. The fully-associative cache avoids any false sharing of entries between branches. Therefore, we can analyze program scopes that contain a number of branches lower than or equal to the number of branches in the prediction table. Experimental results show that the worst-case execution time bounds of programs using the proposed predictor are lower than using static branch predictors at a moderate hardware cost.
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Dates and versions

hal-01976187 , version 1 (09-01-2019)

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Martin Schoeberl, Benjamin Rouxel, Isabelle Puaut. A Time-predictable Branch Predictor. SAC 2019 - 34th ACM/SIGAPP Symposium on Applied Computing, Apr 2019, Limassol, Cyprus. pp.1-10, ⟨10.1145/3297280.3297337⟩. ⟨hal-01976187⟩
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