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

• URL: http://arxiv.org/abs/2305.14968v2
• Date: Thu, 25 May 2023 10:30:19 GMT
• Title: 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
• Abstract summary: EnergyAnalyzer is a code-level static analysis tool for estimating the energy consumption of embedded software. It 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.
• Score: 0.6144680854063939
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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