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

TL;DR

EnergyAnalyzer is a static analysis tool that estimates embedded software energy consumption using WCET analysis techniques and energy models for predictable architectures, aiding energy-aware development.

Contribution

It introduces a novel static analysis approach combining WCET techniques with energy modeling for embedded applications on ARM Cortex-M0 and Gaisler LEON3 architectures.

Findings

Estimated energy consumption differs less than 1% from empirical models.

Validated across numerous benchmarks for two architectures.

Applicable in diverse use cases like neural networks and satellite software.

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.