@misc{10481/102752,
year = {2025},
month = {1},
url = {https://hdl.handle.net/10481/102752},
abstract = {The growing global demand for computational resources, particularly in Artificial
Intelligence (AI) applications, raises increasing concerns about energy consumption and
its environmental impact. This study introduces a newly developed energy consumption
index that evaluates the energy efficiency of Deep Learning (DL) models, providing a
standardized and adaptable approach for various models. Convolutional neural networks,
including both classical and modern architectures, serve as the primary case study to
demonstrate the applicability of the index. Furthermore, the inclusion of the Swin Transformer,
a state-of-the-art and modern non-convolutional model, highlights the adaptability
of the framework to diverse architectural paradigms. This study analyzes the energy
consumption during both training and inference of representative DL architectures, including
AlexNet, ResNet18, VGG16, EfficientNet-B3, ConvNeXt-T, and Swin Transformer,
trained on the Imagenette dataset using TITAN XP and GTX 1080 GPUs. Energy measurements
are obtained using sensor-based tools, including OpenZmeter (v2) with integrated
electrical sensors. Additionally, software-based tools such as CarbonTracker (v1.2.5) and
CodeCarbon (v2.4.1) retrieve energy consumption data from computational component
sensors. The results reveal significant differences in energy efficiency across architectures
and GPUs, providing insights into the trade-offs between model performance and energy
use. By offering a flexible framework for comparing energy efficiency across DL models,
this study advances sustainability in AI systems, supporting accurate and standardized
energy evaluations applicable to various computational settings.},
organization = {PID2023-147409NB-C21. MICIU/AEI/10.13039/501100011033 and by ERDF/EU. Ministerio Español de Ciencia e Innovación},
organization = {PID2020-115570GB-C22. MICIU/AEI/10.13039/501100011033 and by ERDF/EU. Ministerio Español de Ciencia e Innovación},
organization = {PID2022-137461NB-C32. MICIU/AEI/10.13039/501100011033 and by ERDF/EU. Ministerio Español de Ciencia e Innovación},
organization = {TIC251-G-FEDER. ERDF/EU},
organization = {C-ING-027-UGR23. ERDF/EU},
publisher = {MDPI},
keywords = {green computing},
keywords = {energy efficiency},
keywords = {machine learning},
title = {Towards an energy consumption index for deep learning models: a comparative analysis of architectures, GPUs, and measurement Tools},
doi = {10.3390/s25030846},
author = {Aquino Brítez, Sergio and García Sánchez, Pablo and Ortiz, Andrés and Aquino Brítez, Diego},
}