License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.WCET.2023.9
URN: urn:nbn:de:0030-drops-184380
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18438/
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Wegener, Simon ; Nikov, Kris K. ; Nunez-Yanez, Jose ; Eder, Kerstin

EnergyAnalyzer: Using Static WCET Analysis Techniques to Estimate the Energy Consumption of Embedded Applications

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OASIcs-WCET-2023-9.pdf (0.6 MB)

Abstract

This paper presents EnergyAnalyzer, a code-level static analysis tool for estimating the energy consumption of embedded software based on statically predictable hardware events. The tool utilises techniques usually used for worst-case execution time (WCET) analysis together with bespoke energy models developed for two predictable architectures - the ARM Cortex-M0 and the Gaisler LEON3 - to perform energy usage analysis. EnergyAnalyzer has been applied in various use cases, such as selecting candidates for an optimised convolutional neural network, analysing the energy consumption of a camera pill prototype, and analysing the energy consumption of satellite communications software. The tool was developed as part of a larger project called TeamPlay, which aimed to provide a toolchain for developing embedded applications where energy properties are first-class citizens, allowing the developer to reflect directly on these properties at the source code level. The analysis capabilities of EnergyAnalyzer are validated across a large number of benchmarks for the two target architectures and the results show that the statically estimated energy consumption has, with a few exceptions, less than 1% difference compared to the underlying empirical energy models which have been validated on real hardware.

BibTeX - Entry

@InProceedings{wegener_et_al:OASIcs.WCET.2023.9,
  author =	{Wegener, Simon and Nikov, Kris K. and Nunez-Yanez, Jose and Eder, Kerstin},
  title =	{{EnergyAnalyzer: Using Static WCET Analysis Techniques to Estimate the Energy Consumption of Embedded Applications}},
  booktitle =	{21th International Workshop on Worst-Case Execution Time Analysis (WCET 2023)},
  pages =	{9:1--9:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-293-8},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{114},
  editor =	{W\"{a}gemann, Peter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18438},
  URN =		{urn:nbn:de:0030-drops-184380},
  doi =		{10.4230/OASIcs.WCET.2023.9},
  annote =	{Keywords: Energy Modelling, Static Analysis, Gaisler LEON3, ARM Cortex-M0}
}

Keywords: Energy Modelling, Static Analysis, Gaisler LEON3, ARM Cortex-M0
Collection: 21th International Workshop on Worst-Case Execution Time Analysis (WCET 2023)
Issue Date: 2023
Date of publication: 26.07.2023

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