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arXiv · 2301.12806

Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis

Kris Nikov·Kyriakos Georgiou·Zbigniew Chamski·Kerstin Eder·Jose Nunez-Yanez
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Abstract

Energy modelling can enable energy-aware software development and assist the developer in meeting an application's energy budget. Although many energy models for embedded processors exist, most do not account for processor-specific configurations, neither are they suitable for static energy consumption estimation. This paper introduces a set of comprehensive energy models for Arm's Cortex-M0 processor, ready to support energy-aware development of edge computing applications using either profiling- or static-analysis-based energy consumption estimation. We use a commercially representative physical platform together with a custom modified Instruction Set Simulator to obtain the physical data and system state markers used to generate the models. The models account for different processor configurations which all have a significant impact on the execution time and energy consumption of edge computing applications. Unlike existing works, which target a very limited set of applications, all developed models are generated and validated using a very wide range of benchmarks from a variety of emerging IoT application areas, including machine learning and have a prediction error of less than 5%.

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Kris Nikov, Kyriakos Georgiou, Zbigniew Chamski, Kerstin Eder, Jose Nunez-Yanez. 2023-01-30. Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis. https://doi.org/10.1109/icecs202256217.2022.9971086

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