Sustainability of Artificial Intelligence Systems
Description
This research area explores several dimensions of sustainability of Artificial Intelligence (AI) systems, with special emphasis on Machine Learning (ML) systems: environmental, economic, technical, social, and individual sustainability. The research aims to foster sustainability in all the stages of the MLOps/ML lifecycle.
Team
Silverio Martínez-Fernández
UPC (Principal Investigator)
Xavier Franch
UPC
Matias Martinez
UPC
Joel Castaño
UPC - PhD
Vincenzo De Martino
Univesity of Salerno - PhD
Alexandra González
UPC - PhD
Pol Plana
UPC - MSc
Santiago del Rey
UPC - PhD
Projects Overview
| Title | Type | Goal |
|---|---|---|
| Platform for Creating Energy-efficient AI Systems – PRAISE 2025-2028 |
Collaboration Public-Private | Establishing a comprehensive framework to certify that AI systems developed by UPC and Coovally and deployed through the Coovally platform meet European Green AI standards. |
|
Cost Optimization of Artificial Intelligence Systems with Sustainable Practices |
Technology transfer | The project aims to tackle these industrial challenges utilizing the GAISSA-Optimizer tool. It consists of a web-based system that enables AI practitioners: (a) to simulate the return on investment (ROI) of implementing sustainable practices to achieve substantial cost savings; and (b) to integrate sustainable practices in the AI workflow of the company, leading to improved and standardized energy efficiency labels of AI systems. |
| Towards green AI-based software systems: an architecture-centric approach 2022-2025 |
Research | To provide data scientists and software engineers tool-supported, architecture-centric methods for the modeling and development of green AI-based systems. |
| Generation of energy labels for certifying the efficiency of machine learning systems 2024 |
Technology transfer | Studying the viability of tool-supported energy labels for certifying ML systems. |
| Greening AI with Software Engineering 2025 |
Research network | Proposing to connect current developments in computer science applications with the urgency of transitioning to (and maintaining) a sustainable society. |
| Audiovisual and digital material for data engineering 2021-2022 |
Teaching innovation | Defining and improving university courses regarding the technical sustainability of ML systems in the MLOps lifecycle, including best software engineering practices for ML systems. |
Outcomes/Main Contributions
Research:
- Environmental sustainability of ML systems (Green AI) with a software engineering perspective aiming to reduce the environmental impact in the ML lifecycle by:
- Reporting guidelines of ML carbon emissions during training.
- Reducing ML model size and using lightweight architectures.
- Applying interactive mid-training tactics such as early stopping.
- Evidence-based decision making on where to execute (in terms of cloud-edge continuum and hardware).
- Optimizing the selection of serving infrastructure (e.g., runtime engines, execution providers).
- Aggregation of empirical evidence on ML model quantization.
- Applying architectural tactics.
- Technical Sustainability of ML Systems:
- Economic Sustainability of ML Systems:
- Social Sustainability of ML Systems:
Technology Transfer:
- Tools for environmental and economic sustainability of ML systems, such as:
- the GAISSALabel tool for energy efficiency label for ML systems.
- the GAISSA ROI Analyzer.
- Contributions to standards activities such as:
- “ISO/IEC DTR 20226 Information technology — Artificial intelligence — Environmental sustainability aspects of AI systems”
- The Spanish UNE working group CTN-UNE 71/SC 42/GT 1 "Evaluación de la eficiencia energética de los sistemas de inteligencia artificial": "Especificación UNE 0086 Medición del consumo energético, huella de carbono, consumo del agua y rendimiento de sistemas de Inteligencia Artificial".
Teaching:
- The state-of-the-art outcomes of this research area (and the SE4AI research area) are integrated into two subjects:
- “Machine Learning Systems in Production” (MLOps) of the Master of Data Science at UPC.
- “Advanced Topics of Data Engineering 2” (TAED2) of the Bachelor degree of Data Science and Engineering at UPC.
Community:
- Co-organizers of the 2024 and 2025 editions of the International Workshop on Green and Sustainable Software (GREENS).
Team Leader and Contact Person
Silverio Martínez-Fernández
Contact Silverio Martínez-Fernández
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