Data-driven Software-based Power Estimation for Embedded Devices
Haoyu Wang, Xinyi Li, Ti Zhou, Man Lin

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…
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Taxonomy
TopicsEmbedded Systems Design Techniques · Low-power high-performance VLSI design
