@inproceedings{MF21a,
Author = {Kateryna Muts and Heiko Falk},
Title = {Predicting Worst-Case Execution Times During Multi-Criterial Function Inlining.},
Year = {(2021).},
Month = {October},
Note = {kmuts, hfalk, multiopt, ESD, WCC},
Isbn = {10.1007/978-3-030-95467-3_21},
Howpublished = {21-75 MF21a LOD},
Booktitle = {<em>In Proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science (LOD)</em>},
Abstract = {In the domain of hard real-time systems, the Worst-Case Execution Time (WCET) is one of the most important design criteria. Safely and accurately estimating the WCET during a static WCET analysis is computationally demanding because of the involved data flow, control flow, and microarchitecture analyses. This becomes critical in the field of multi-criterial compiler optimizations that trade the WCET with other design objectives. Evolutionary algorithms are typically exploited to solve a multi-objective optimization problem, but they require an extensive evaluation of the objectives to explore the search space of the problem. This paper proposes a method that utilizes machine learning to build a surrogate model in order to quickly predict the WCET instead of costly estimating it using static WCET analysis. We build a prediction model that is independent of the source code and assembly code features, so a compiler can utilize it to perform any compiler-based optimization. We demonstrate the effectiveness of our model on multi-criterial function inlining, where we aim to explore trade-offs between the WCET, code size, and energy consumption at compile time.}
}

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