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

• Published in: arXiv.org
• Main Authors: Wegener, Simon, Nikov, Kris K, Nunez-Yanez, Jose, Eder, Kerstin
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 25.05.2023
• Subjects: Artificial neural networks; Computer Science - Software Engineering; Embedded systems; Empirical analysis; Energy consumption; Hardware; Satellite communications; Software; Source code; Static code analysis
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2305.14968

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.