Data-driven Software-based Power Estimation for Embedded Devices

TL;DR

This paper presents a data-driven, software-based energy estimation approach for IoT devices using machine learning and physical measurements, achieving 92% accuracy on embedded boards like Jetson Nano and Raspberry Pi.

Contribution

It introduces a novel, cost-effective method combining machine learning, physical measurements, and kernel modules for accurate power estimation of embedded devices.

Findings

Achieved 92% accuracy in power estimation.

Demonstrated effectiveness on Jetson Nano and Raspberry Pi.

Developed a kernel module for real-time power prediction.

Abstract

Energy measurement of computer devices, which are widely used in the Internet of Things (IoT), is an important yet challenging task. Most of these IoT devices lack ready-to-use hardware or software for power measurement. In this paper, we propose an easy-to-use approach to derive a software-based energy estimation model with external low-end power meters based on data-driven analysis. Our solution is demonstrated with a Jetson Nano board and Ruideng UM25C USB power meter. Various machine learning methods combined with our smart data collection & profiling method and physical measurement are explored. Periodic Long-duration measurements are utilized in the experiments to derive and validate power models, allowing more accurate power readings from the low-end power meter. Benchmarks were used to evaluate the derived software-power model for the Jetson Nano board and Raspberry Pi. The results show that 92\% accuracy can be achieved by the software-based power estimation compared to measurement. A kernel module that can collect running traces of utilization and frequencies needed is developed, together with the power model derived, for power prediction for programs running in a real environment. Our cost-effective method facilitates accurate instantaneous power estimation, which low-end power meters cannot directly provide.