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Data-Driven Software-Based Power Estimation for Embedded Devices
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DOI：10.1109/TSUSC.2025.3567856.png)
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摘要
En 中文
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.
Keyword:
Meters
Energy consumption
Energy measurement
Power measurement
Benchmark testing
Accuracy
Estimation
Hardware
Data models
Program processors
Power estimation
smart profiling
machine learning
期刊
I
IF:
3.9
论文数:
91
被引数:
0
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