A Comprehensive and Accurate Energy Model for Arm's Cortex-M0 Processor

TL;DR

This paper presents a detailed energy model for Arm's Cortex-M0 processor that accurately estimates energy consumption considering various configurations, aiding energy-aware development of edge computing applications.

Contribution

The paper introduces a comprehensive energy model for Cortex-M0 that incorporates processor-specific configurations and achieves less than 5% prediction error.

Findings

Prediction error less than 5% for all models

Supports both profiling and static analysis methods

Accounts for Frequency, PreFetch, and WaitState configurations

Abstract

Energy modeling 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 comprehensive energy model 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. The model accounts for the Frequency, PreFetch, and WaitState processor configurations which all have a significant impact on the execution time and energy consumption of edge computing applications. All models have a prediction error of less than 5%.