Please use this identifier to cite or link to this item:
https://doi.org/10.15480/336.4890
Title: | Multiobjective code compression and function inlining for hard real-time systems | Language: | English | Authors: | Muts, Kateryna ![]() |
Keywords: | Compiler-based optimization; Function Inlining; Machine learning; Search space reduction; Hard real-time system | Issue Date: | 26-Jan-2023 | Abstract (english): | Compiler-based optimizations are efficient techniques to improve a program to be compiled. Function inlining is a well-known compiler-based optimization that substitutes function calls by the body of the function. The data represent the results of multiobjective function inlining for hard real-time systems with code size, energy consumption and worst-case execution time (WCET) as objectives. Since the analyses of energy consumption and WCET are very time-consuming at compile time, search space reduction and predictions based on machine learning techniques were applied to speed up the multiobjective function inlining at compile time. |
URI: | http://hdl.handle.net/11420/14664 | DOI: | 10.15480/336.4890 | Institute: | Eingebettete Systeme E-13 | Document Type: | Dataset | Project: | Multikriterielle Code-Optimierung für Eingebettete Harte Echtzeitsysteme | Funded by: | Deutsche Forschungsgemeinschaft (DFG) | License: | ![]() |
Is supplement to: | 10.15480/882.4799 | Is compiled by: | 10.1007/s11241-010-9101-x |
Appears in Collections: | Research Data TUHH |
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File | Description | Size | Format | |
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README.pdf | 32,19 kB | Adobe PDF | View/Open![]() | |
MultiOpt-compression-funinlining.zip | 3,32 MB | ZIP | View/Open |
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