Estimation of energy consumption in machine learning
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Posts by Collection
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Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
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Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
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A systematic review of Green AI
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Tensor-Aware Energy Accounting
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publications
Specification-Driven Code Generation for Inter-parameter Dependencies in Web APIs
Authors: Saman Barakat, Enrique Barba Roque, Ana Belén Sánchez, Sergio Segura
Published in Service-Oriented Computing – ICSOC 2022 Workshops, 2022
Automatic code generation for APIs considering inter-parameter dependencies.
Recommended citation: Barakat, S., Barba-Roque, E., Sánchez, A.B., Segura, S. (2022). "Secification-Driven Code Generation for Inter-parameter Dependencies in Web APIs." Service-Oriented Computing – ICSOC 2022 Workshops.
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Unveiling the Energy Vampires: A Methodology for Debugging Software Energy Consumption
🏆 ACM SIGSOFT Distinguished Paper AwardAuthors: Enrique Barba Roque, Luis Cruz, Thomas Durieux
Published in International Conference on Software Engineering, Ottawa, Canada, 2025
Energy debugging methodology for identifying and isolating energy consumption hotspots in software systems, with Alpine consuming up to 20.2% more power than Ubuntu in certain operations.
talks
Finding Energy Inefficiencies in shared libraries with Docker
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I share my work on my Master Thesis on finding energy inefficiencies in different Linux distributions and libc implementations with the KDE Eco community, focused on improving energy efficiency of software. Minutes, Slides