Accurate Calibration of Power Measurements from Internal Power Sensors on NVIDIA Jetson Devices

TL;DR

This paper presents a calibration method for NVIDIA Jetson device power sensors, significantly improving their accuracy from up to 50% underestimation to within 3%, enabling reliable real-time power monitoring.

Contribution

It introduces a regression-based calibration approach for internal power sensors on Jetson devices, enhancing measurement accuracy for energy-efficient edge computing applications.

Findings

Internal sensors underestimate power by up to 50%.

Calibration reduces error to within 3%.

Calibrated sensors enable precise real-time power assessment.

Abstract

Power efficiency is a crucial consideration for embedded systems design, particularly in the field of edge computing and IoT devices. This study aims to calibrate the power measurements obtained from the built-in sensors of NVIDIA Jetson devices, facilitating the collection of reliable and precise power consumption data in real-time. To achieve this goal, accurate power readings are obtained using external hardware, and a regression model is proposed to map the sensor measurements to the true power values. Our results provide insights into the accuracy and reliability of the built-in power sensors for various Jetson edge boards and highlight the importance of calibrating their internal power readings. In detail, internal sensors underestimate the actual power by up to 50% in most cases, but this calibration reduces the error to within 3%. By making the internal sensor data usable for precise online assessment of power and energy figures, the regression models presented in this paper have practical applications, for both practitioners and researchers, in accurately designing energy-efficient and autonomous edge services.