Predicting Worst-Case Execution Times During Multi-criterial Function Inlining

Publikationstyp
Conference Paper
Date Issued
2021-10
Sprache
English
Author(s)
Muts, Kateryna  orcid-logo
Falk, Heiko  orcid-logo
Institut
Eingebettete Systeme E-13  
TORE-URI
http://hdl.handle.net/11420/11829
First published in
Lecture notes in computer science  
Number in series
13163 LNCS
Start Page
281
End Page
295
Citation
International Conference on Machine Learning, Optimization, and Data Science (LOD 2021)
Contribution to Conference
7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021  
Publisher DOI
10.1007/978-3-030-95467-3_21
Scopus ID
2-s2.0-85125245038
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.
Subjects
Classification
Compiler-based optimization
Hard real-time system
Multi-objective optimization