CAIN 2024
Sun 14 - Mon 15 April 2024 Lisbon, Portugal
co-located with ICSE 2024

The widespread integration of Machine Learning (ML) in software systems has brought forth unprecedented advancements, yet the surge in energy consumption raises ecological concerns. This research addresses the environmental impact of ML development, focusing on the energy implications of design decisions in ML-based systems. This thesis aims to offer insights into the energy consumption patterns influenced by decisions such as deployment and training location. Different case studies on ML-based systems will be conducted to validate and demonstrate the implications of these design choices. The expected outcomes encompass actionable insights, validated through rigorous evaluations, and the development of a decision support tool for ML-based system development, with energy efficiency at its core. This work contributes to the broader field of Green AI by addressing a critical gap and guiding the transition towards a more sustainable AI landscape.

Mon 15 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
Doctoral Symposium and Energy-Aware AI EngineeringDoctoral Symposium / Research and Experience Papers at Pequeno Auditório
Chair(s): Justus Bogner Vrije Universiteit Amsterdam, Silverio Martínez-Fernández UPC-BarcelonaTech
11:00
6m
Talk
Software Design Decisions for Greener Machine Learning-based Systems
Doctoral Symposium
Santiago del Rey Universitat Politècnica de Catalunya (UPC)
11:06
6m
Talk
Energy-Efficient Development of ML-Enabled Systems: A Data-Centric Approach
Doctoral Symposium
11:12
6m
Talk
Optimizing Data Analytics Workflows through User-driven Experimentation
Doctoral Symposium
Keerthiga Rajenthiram Vrije Universiteit Amsterdam
11:18
6m
Talk
Component-based Approach to Software Engineering of Machine Learning-enabled Systems
Doctoral Symposium
Vladislav Indykov Chalmers | University of Gothenburg
11:24
6m
Talk
Threat Modeling of ML-intensive Systems: Research Proposal
Doctoral Symposium
Felix Viktor Jedrzejewski Blekinge Institute of Technology
11:30
6m
Talk
Continuous Quality Assurance ML Pipelines under the AI Act
Doctoral Symposium
Matthias Wagner Lund University
11:36
10m
Talk
Green Runner: A tool for efficient deep learning component selection
Research and Experience Papers
Jai Kannan Applied Artificial Intelligence Institute, Deakin University, Scott Barnett Applied Artificial Intelligence Institute, Deakin University, Anj Simmons , Taylan Selvi Applied Artificial Intelligence Institute, Deakin University, Luís Cruz Delft University of Technology
11:46
15m
Talk
Engineering Carbon Emission-aware Machine Learning Pipelines
Research and Experience Papers
Erik Johannes Husom SINTEF Digital, Sagar Sen , Arda Goknil SINTEF Digital
12:01
10m
Talk
Identifying architectural design decisions for achieving green ML serving
Research and Experience Papers
Francisco Durán Universitat Politècnica De Catalunya - Barcelona Tech, Silverio Martínez-Fernández UPC-BarcelonaTech, Matias Martinez Universitat Politècnica de Catalunya (UPC), Patricia Lago Vrije Universiteit Amsterdam
Pre-print
12:11
10m
Talk
Green AI: a Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
Research and Experience Papers
Negar Alizadeh Universiteit Utrecht, Fernando Castor University of Twente and Federal University of Pernambuco
12:21
9m
Live Q&A
Energy: Q&A Session
Research and Experience Papers