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

arXiv: https://arxiv.org/abs/2506.01774 · HTML: https://mlsanity.com/paper/2506.01774/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 26 of 26)

- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [MLPerf Inference Benchmark](https://mlsanity.com/paper/1911.02549/) (2019)
- [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [MLPerf Training Benchmark](https://mlsanity.com/paper/1910.01500/) (2019)
- [How to estimate carbon footprint when training deep learning models? A guide and review](https://mlsanity.com/paper/2306.08323/) (2023)
- [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022)
- [A Synthesis of Green Architectural Tactics for ML-Enabled Systems](https://mlsanity.com/paper/2312.09610/) (2023)
- [Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI](https://mlsanity.com/paper/2309.02065/) (2023)
- [IrEne: Interpretable Energy Prediction for Transformers](https://mlsanity.com/paper/2106.01199/) (2021)
- [EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search](https://mlsanity.com/paper/2210.06015/) (2022)
- [Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures](https://mlsanity.com/paper/2402.13640/) (2024)
- [Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement](https://mlsanity.com/paper/2308.12264/) (2023)
- [CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework](https://mlsanity.com/paper/2406.01414/) (2024)
- [Batching for Green AI -- An Exploratory Study on Inference](https://mlsanity.com/paper/2307.11434/) (2023)
- [PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/2403.12562/) (2024)
- [Towards Architecting Sustainable MLOps: A Self-Adaptation Approach](https://mlsanity.com/paper/2404.04572/) (2024)
- [EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems](https://mlsanity.com/paper/2404.11411/) (2024)
- [Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy](https://mlsanity.com/paper/2412.00329/) (2024)
- [Green AI in Action: Strategic Model Selection for Ensembles in Production](https://mlsanity.com/paper/2405.17451/) (2024)
- [Sustainability in Computing Education: A Systematic Literature Review](https://mlsanity.com/paper/2305.10369/) (2023)
- [Tensor-Aware Energy Accounting](https://mlsanity.com/paper/2311.11424/) (2023)
- [Using Sustainability Impact Scores for Software Architecture Evaluation](https://mlsanity.com/paper/2501.17004/) (2025)

## Built on by (most cited first, 8 of 8)

- [SWEnergy: An Empirical Study on Energy Efficiency in Agentic Issue Resolution Frameworks with SLMs](https://mlsanity.com/paper/2512.09543/) (2025)
- [Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizations](https://mlsanity.com/paper/2506.18289/) (2025)
- [Choosing to Be Green: Advancing Green AI via Dynamic Model Selection](https://mlsanity.com/paper/2509.19996/) (2025)
- [Towards Sustainability Model Cards](https://mlsanity.com/paper/2507.19559/) (2025)
- [SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models](https://mlsanity.com/paper/2507.15663/) (2025)
- [Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development](https://mlsanity.com/paper/2602.19718/) (2026)
- [From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot](https://mlsanity.com/paper/2606.10861/) (2026)
- [FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment](https://mlsanity.com/paper/2608.14550/) (2026)

## 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.