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WE-HML: hybrid WCET estimation using machine learning for architectures with caches

Abstract : Modern processors raise a challenge for WCET estimation, since detailed knowledge of the processor microarchitecture is not available. This paper proposes a novel hybrid WCET estimation technique, WE-HML, in which the longest path is estimated using static techniques, whereas machine learning (ML) is used to determine the WCET of basic blocks. In contrast to existing literature using ML techniques for WCET estimation, WE-HML (i) operates on binary code for improved precision of learning, as compared to the related techniques operating at source code or intermediate code level; (ii) trains the ML algorithms on a large set of automatically generated programs for improved quality of learning; (iii) proposes a technique to take into account data caches. Experiments on an ARM Cortex-A53 processor show that for all benchmarks, WCET estimates obtained by WE-HML are larger than all possible execution times. Moreover, the cache modeling technique of WE-HML allows an improvement of 65 percent on average of WCET estimates compared to its cache-agnostic equivalent.
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Contributor : Abderaouf Nassim AMALOU Connect in order to contact the contributor
Submitted on : Wednesday, July 7, 2021 - 10:57:10 AM
Last modification on : Sunday, June 26, 2022 - 2:20:46 AM
Long-term archiving on: : Friday, October 8, 2021 - 6:13:46 PM


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  • HAL Id : hal-03280177, version 1


Abderaouf Nassim Amalou, Isabelle Puaut, Gilles Muller. WE-HML: hybrid WCET estimation using machine learning for architectures with caches. RTCSA 2021 - 27th IEEE International Conference on Embedded Real-Time Computing Systems and Applications, Aug 2021, Online Virtual Conference, France. pp.1-10. ⟨hal-03280177⟩



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