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

arXiv: https://arxiv.org/abs/2301.12806 · HTML: https://mlsanity.com/paper/2301.12806/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2023.

## Built on by (most cited first, 1 of 1)

- [InstMeter: An Instruction-Level Method to Predict Energy and Latency of DL Model Inference on MCUs](https://mlsanity.com/paper/2603.04134/) (2026)

## Abstract (arXiv)

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%.