Accurate Calibration of Power Measurements from Internal Power Sensors on NVIDIA Jetson Devices
Neda Shalavi, Aria Khoshsirat, Marco Stellini, Andrea Zanella, Michele, Rossi

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…
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Taxonomy
TopicsGreen IT and Sustainability · Smart Grid Energy Management · Energy Harvesting in Wireless Networks
