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

TL;DR

This paper presents detailed energy models for the Cortex-M0 processor that support both profiling and static analysis, accounting for configurations and validated across diverse IoT benchmarks with high accuracy.

Contribution

It introduces comprehensive, configurable energy models for Cortex-M0, validated with a wide range of benchmarks, enabling accurate energy estimation for edge computing applications.

Findings

Models achieve less than 5% prediction error.

Account for various processor configurations.

Validated with diverse IoT benchmarks.

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%.