mlsanityData to 7 Oct 2026

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices

arXiv 2506.01774 ↗First posted 2 Jun 2025cs.SE cs.AIcited by 8

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Abstract (arXiv)

The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions. The "Greening AI with Software Engineering" CECAM-Lorentz workshop (no. 1358, 2025) funded by the Centre Europ'een de Calcul Atomique et Mol'eculaire and the Lorentz Center, provided an interdisciplinary forum for 29 participants, from practitioners to academics, to share knowledge, ideas, practices, and current results dedicated to advancing green software and AI research. The workshop was held February 3-7, 2025, in Lausanne, Switzerland. Through keynotes, flash talks, and collaborative discussions, participants identified and prioritized key challenges for the field. These included energy assessment and standardization, benchmarking practices, sustainability-aware architectures, runtime adaptation, empirical methodologies, and education. This report presents a research agenda emerging from the workshop, outlining open research directions and practical recommendations to guide the development of environmentally sustainable AI-enabled systems rooted in software engineering principles.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 26 of its 53 references that are in the map. First posted on arXiv 2 Jun 2025.

Builds on

26 of 53 references.

References · 26

  1. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 778
  2. 2019Green AIcited by 620
  3. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 264
  4. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
  5. 2019MLPerf Inference Benchmarkcited by 195
  6. 2023Power Hungry Processing: Watts Driving the Cost of AI Deployment?cited by 186
  7. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 168
  8. 2019MLPerf Training Benchmarkcited by 114
  9. 2023How to estimate carbon footprint when training deep learning models? A guide and reviewcited by 34
  10. 2022Data-Centric Green AI: An Exploratory Empirical Studycited by 24
  11. 2023A Synthesis of Green Architectural Tactics for ML-Enabled Systemscited by 17
  12. 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
14 more
  1. 2021IrEne: Interpretable Energy Prediction for Transformerscited by 11
  2. 2022EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Searchcited by 11
  3. 2024Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructurescited by 11
  4. 2023Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurementcited by 5
  5. 2024CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Frameworkcited by 5
  6. 2023Batching for Green AI -- An Exploratory Study on Inferencecited by 4
  7. 2024PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysiscited by 4
  8. 2024Towards Architecting Sustainable MLOps: A Self-Adaptation Approachcited by 4
  9. 2024EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systemscited by 3
  10. 2024Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracycited by 3
  11. 2024Green AI in Action: Strategic Model Selection for Ensembles in Productioncited by 2
  12. 2023Sustainability in Computing Education: A Systematic Literature Reviewcited by 1
  13. 2023Tensor-Aware Energy Accountingcited by 1
  14. 2025Using Sustainability Impact Scores for Software Architecture Evaluationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025SWEnergy: An Empirical Study on Energy Efficiency in Agentic Issue Resolution Frameworks with SLMscited by 5
  2. 2025Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizationscited by 2
  3. 2025Choosing to Be Green: Advancing Green AI via Dynamic Model Selectioncited by 2
  4. 2025Towards Sustainability Model Cardscited by 1
  5. 2025SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Modelscited by 0
  6. 2026Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Developmentcited by 0
  7. 2026From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbotcited by 0
  8. 2026FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessmentcited by 0