76 citations · 619 across the 31 of their papers we have counts for
5 papers · 1 filter
Estimating Deep Learning energy consumption based on model architecture and training environment
Santiago del Rey, Luís Cruz, Xavier Franch +1
To raise awareness of the environmental impact of deep learning (DL), many studies estimate the energy use of DL systems. However, energy estimates during DL training often rely on…
Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study
Joel Castaño, Silverio Martínez-Fernández, Xavier Franch +1
The rise of machine learning (ML) systems has exacerbated their carbon footprint due to increased capabilities and model sizes. However, there is scarce knowledge on how the carbon…
Energy Efficiency of Training Neural Network Architectures: An Empirical Study
Yinlena Xu, Silverio Martínez-Fernández, Matias Martinez +1
The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational pow…
Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?
Roger Creus Castanyer, Silverio Martínez-Fernández, Xavier Franch
Background: The construction, evolution and usage of complex artificial intelligence (AI) models demand expensive computational resources. While currently available high-performanc…
Integration of Convolutional Neural Networks in Mobile Applications
Roger Creus Castanyer, Silverio Martínez-Fernández, Xavier Franch
When building Deep Learning (DL) models, data scientists and software engineers manage the trade-off between their accuracy, or any other suitable success criteria, and their compl…