Sieh V, Burlacu R, Hönig T, Janker H, Raffeck P, Wägemann P, Schröder-Preikschat W (2019)
Publication Type: Journal article
Publication year: 2019
Book Volume: 11
Pages Range: 38-41
Journal Issue: 2
URI: https://www4.cs.fau.de/Publications/2019/sieh_19_esl.pdf
Open Access Link: https://www4.cs.fau.de/Publications/2019/sieh_19_esl.pdf
Predicting the temporal behavior of embedded real-time systems is a crucial but challenging task, as it is with the energetic behavior of energy-constrained systems, such as IoT devices. To carry out static analyses in order to determine the worst-case execution time or the worst-case energy consumption of tasks, cost models are inevitable. However, these models are rarely available on a fine-grained level for commercial-off-the-shelf hardware platforms. In this letter, we present NEO, an end-to-end toolchain that automatically generates cost models, which are then integrated into an existing static-analysis tool. NEO exploits automatically generated benchmark programs, which are measured on the target platform and investigated in a virtual machine. Based on the gathered data, we formulate mathematical optimization problems that eventually yield both worst-case execution-time and energy-consumption cost models. In our evaluations with an embedded hardware platform (e.g., ARM Cortex-M0+), we show that the open-source toolchain is able to precisely bound programs' resources while achieving acceptable accuracy.
APA:
Sieh, V., Burlacu, R., Hönig, T., Janker, H., Raffeck, P., Wägemann, P., & Schröder-Preikschat, W. (2019). Combining Automated Measurement-Based Cost Modeling With Static Worst-Case Execution-Time and Energy-Consumption Analyses. IEEE Embedded Systems Letters, 11(2), 38-41. https://doi.org/10.1109/LES.2018.2868823
MLA:
Sieh, Volkmar, et al. "Combining Automated Measurement-Based Cost Modeling With Static Worst-Case Execution-Time and Energy-Consumption Analyses." IEEE Embedded Systems Letters 11.2 (2019): 38-41.
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