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%.

翻译：能耗建模能够支持能耗感知的软件开发，并帮助开发者满足应用程序的能耗预算。尽管针对嵌入式处理器已存在多种能耗模型，但大多数模型未考虑处理器特定配置，也不适用于静态能耗估算。本文为Arm的Cortex-M0处理器提出一套全面的能耗模型，可支持基于性能剖析或静态分析的能耗估算方法，助力边缘计算应用的能耗感知开发。我们采用商业代表性物理平台，结合定制修改的指令集模拟器，获取用于生成模型所需的物理数据与系统状态标记。这些模型考虑了多种处理器配置，这些配置对边缘计算应用的执行时间和能耗均具有显著影响。与现有针对非常有限应用场景的研究不同，本文所有模型均基于来自机器学习等新兴物联网应用领域的广泛基准测试进行生成与验证，预测误差低于5%。