# A Synthesis of Green Architectural Tactics for ML-Enabled Systems

arXiv: https://arxiv.org/abs/2312.09610 · HTML: https://mlsanity.com/paper/2312.09610/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 69 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 27 of 27)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://mlsanity.com/paper/1611.05128/) (2016)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Optimizing the energy consumption of spiking neural networks for neuromorphic applications](https://mlsanity.com/paper/1912.01268/) (2019)
- [E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings](https://mlsanity.com/paper/1910.13349/) (2019)
- [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022)
- [Energy-Aware Analog Aggregation for Federated Learning with Redundant Data](https://mlsanity.com/paper/1911.00188/) (2019)
- [Towards Guidelines for Assessing Qualities of Machine Learning Systems](https://mlsanity.com/paper/2008.11007/) (2020)
- [Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI](https://mlsanity.com/paper/2303.13972/) (2023)
- [Energy Efficiency of Training Neural Network Architectures: An Empirical Study](https://mlsanity.com/paper/2302.00967/) (2023)
- [Software Architecture for ML-based Systems: What Exists and What Lies Ahead](https://mlsanity.com/paper/2103.07950/) (2021)
- [Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking](https://mlsanity.com/paper/1806.04321/) (2018)
- [IrEne: Interpretable Energy Prediction for Transformers](https://mlsanity.com/paper/2106.01199/) (2021)
- [On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks](https://mlsanity.com/paper/2111.07911/) (2021)
- [Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models](https://mlsanity.com/paper/2204.07288/) (2022)
- [Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository](https://mlsanity.com/paper/2303.13173/) (2023)
- [Estimating Deep Learning energy consumption based on model architecture and training environment](https://mlsanity.com/paper/2307.05520/) (2023)
- [NeuPart: Using Analytical Models to Drive Energy-Efficient Partitioning of CNN Computations on Cloud-Connected Mobile Clients](https://mlsanity.com/paper/1905.05011/) (2019)
- [Sparse Optimization for Green Edge AI Inference](https://mlsanity.com/paper/2002.10080/) (2020)
- [Energy-Efficient AI over a Virtualized Cloud Fog Network](https://mlsanity.com/paper/2105.03221/) (2021)
- [Energy-Aware DNN Graph Optimization](https://mlsanity.com/paper/2005.05837/) (2020)

## Built on by (most cited first, 17 of 17)

- [Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices](https://mlsanity.com/paper/2506.01774/) (2025)
- [How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection](https://mlsanity.com/paper/2404.19452/) (2024)
- [Innovating for Tomorrow: The Convergence of SE and Green AI](https://mlsanity.com/paper/2406.18142/) (2024)
- [Balancing Progress and Responsibility: A Synthesis of Sustainability Trade-Offs of AI-Based Systems](https://mlsanity.com/paper/2404.03995/) (2024)
- [Towards Green AI: Current status and future research](https://mlsanity.com/paper/2407.10237/) (2024)
- [Impact of ML Optimization Tactics on Greener Pre-Trained ML Models](https://mlsanity.com/paper/2409.12878/) (2024)
- [Choosing to Be Green: Advancing Green AI via Dynamic Model Selection](https://mlsanity.com/paper/2509.19996/) (2025)
- [On the Effectiveness of Proposed Techniques to Reduce Energy Consumption in RAG Systems: A Controlled Experiment](https://mlsanity.com/paper/2601.02522/) (2026)
- [The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems](https://mlsanity.com/paper/2407.02914/) (2024)
- [Aggregating empirical evidence from data strategy studies: a case on model quantization](https://mlsanity.com/paper/2505.00816/) (2025)
- [How Do Companies Manage the Environmental Sustainability of AI? An Interview Study About Green AI Efforts and Regulations](https://mlsanity.com/paper/2505.07317/) (2025)
- [Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy](https://mlsanity.com/paper/2506.13838/) (2025)
- [SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs](https://mlsanity.com/paper/2509.20491/) (2025)
- [HarmonE: A Self-Adaptive Approach to Architecting Sustainable MLOps](https://mlsanity.com/paper/2505.13693/) (2025)
- [SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models](https://mlsanity.com/paper/2507.15663/) (2025)
- [KGroups: A Versatile Univariate Max-Relevance Min-Redundancy Feature Selection Algorithm for High-dimensional Biological Data](https://mlsanity.com/paper/2603.28417/) (2026)
- [RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems](https://mlsanity.com/paper/2603.28735/) (2026)

## Abstract (arXiv)

The rapid adoption of artificial intelligence (AI) and machine learning (ML) has generated growing interest in understanding their environmental impact and the challenges associated with designing environmentally friendly ML-enabled systems. While Green AI research, i.e., research that tries to minimize the energy footprint of AI, is receiving increasing attention, very few concrete guidelines are available on how ML-enabled systems can be designed to be more environmentally sustainable. In this paper, we provide a catalog of 30 green architectural tactics for ML-enabled systems to fill this gap. An architectural tactic is a high-level design technique to improve software quality, in our case environmental sustainability. We derived the tactics from the analysis of 51 peer-reviewed publications that primarily explore Green AI, and validated them using a focus group approach with three experts. The 30 tactics we identified are aimed to serve as an initial reference guide for further exploration into Green AI from a software engineering perspective, and assist in designing sustainable ML-enabled systems. To enhance transparency and facilitate their widespread use and extension, we make the tactics available online in easily consumable formats. Wide-spread adoption of these tactics has the potential to substantially reduce the societal impact of ML-enabled systems regarding their energy and carbon footprint.