Muts, KaterynaKaterynaMutsFalk, HeikoHeikoFalk2022-03-072022-03-072021-10International Conference on Machine Learning, Optimization, and Data Science (LOD 2021)http://hdl.handle.net/11420/11829In 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.enClassificationCompiler-based optimizationHard real-time systemMulti-objective optimizationConference Paper10.1007/978-3-030-95467-3_21Other