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

Authors Simon Wegener , Kris K. Nikov , Jose Nunez-Yanez , Kerstin Eder

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Author Details

Simon Wegener
  • AbsInt Angewandte Informatik GmbH, Saarbrücken, Germany
Kris K. Nikov
  • University of Bristol, UK
Jose Nunez-Yanez
  • Linköping University, Sweden
Kerstin Eder
  • University of Bristol, UK

Funding

This work was supported by the H2020 project TeamPlay, Grant Agreement No. 779882.

Acknowledgements

The authors like to thank Marcos Martinez de Alejandro, Nikos Fragoulis, Ali Sahafi, and Vangelis Vassalos for their work on the use cases and Heiko Falk and Shashank Jadhav for the integration of EnergyAnalyzer into WCC.

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Simon Wegener, Kris K. Nikov, Jose Nunez-Yanez, and Kerstin Eder. EnergyAnalyzer: Using Static WCET Analysis Techniques to Estimate the Energy Consumption of Embedded Applications. In 21th International Workshop on Worst-Case Execution Time Analysis (WCET 2023). Open Access Series in Informatics (OASIcs), Volume 114, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/OASIcs.WCET.2023.9

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.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Software verification and validation
  • Software and its engineering → Automated static analysis
Keywords
  • Energy Modelling
  • Static Analysis
  • Gaisler LEON3
  • ARM Cortex-M0

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