88 citations · 135 across the 7 of their papers we have counts for
4 papers · 1 filter
Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
Lorena Poenaru-Olaru, June Sallou, Luis Cruz +2
The reliability of machine learning (ML) software systems is heavily influenced by changes in data over time. For that reason, ML systems require regular maintenance, typically bas…
Batching for Green AI -- An Exploratory Study on Inference
Tim Yarally, Luís Cruz, Daniel Feitosa +2
The batch size is an essential parameter to tune during the development of new neural networks. Amongst other quality indicators, it has a large degree of influence on the model's…
Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
Tim Yarally, Luís Cruz, Daniel Feitosa +2
Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results" often refers to the achieved accuracy on a competitive pro…
Data-Centric Green AI: An Exploratory Empirical Study
Roberto Verdecchia, Luís Cruz, June Sallou +3
With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing…