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

• Published in: 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS) pp. 1 - 4
• Main Authors: Nikov, Kris, Georgiou, Kyriakos, Chamski, Zbigniew, Eder, Kerstin, Nunez-Yanez, Jose
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.10.2022
• Subjects: Analytical models; Arm Cortex-M0; Computational modeling; edge computing; embedded systems; Energy consumption; energy modelling; Estimation; IoT; Machine learning; Predictive models; Static analysis
• ISBN: 9781665488242; 1665488239; 1665488247; 9781665488235
• DOI: 10.1109/ICECS202256217.2022.9971086

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 config-urations, 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%.