mlsanityData to 14 Oct 2026

Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI

arXiv 2208.00406 ↗First posted 31 Jul 2022cs.LG cs.AI cs.CEcited by 58

Not on the map. Why?

Abstract (arXiv)

The size and complexity of deep neural networks continue to grow exponentially, significantly increasing energy consumption for training and inference by these models. We introduce an open-source package eco2AI to help data scientists and researchers to track energy consumption and equivalent CO2 emissions of their models in a straightforward way. In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. We encourage research community to search for new optimal Artificial Intelligence (AI) architectures with a lower computational cost. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 31 Jul 2022.

Builds on

18 of 37 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  4. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  5. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  6. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  7. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  8. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  9. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  10. 20218-bit Optimizers via Block-wise Quantizationcited by 403
  11. 2020Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stoppingcited by 384
  12. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 249
6 more
  1. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 169
  2. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  3. 2016GANS for Sequences of Discrete Elements with the Gumbel-softmax Distributioncited by 48
  4. 2022Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reductioncited by 14
  5. 2022Survey on Large Scale Neural Network Trainingcited by 4
  6. 2021Emojich -- zero-shot emoji generation using Russian language: a technical reportcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 14; 2024: 13; 2025: 17; 2026: 12.

58 papers.

Sort:

By area:

  1. 2023How to estimate carbon footprint when training deep learning models? A guide and reviewcited by 34
  2. 2023Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Studycited by 23
  3. 2025Call for Action: towards the next generation of symbolic regression benchmarkcited by 17
  4. 2023Watt For What: Rethinking Deep Learning's Energy-Performance Relationshipcited by 16
  5. 2024Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Trainingcited by 15
  6. 2023Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecyclecited by 14
  7. 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
  8. 2025From Prompts to Power: Measuring the Energy Footprint of LLM Inferencecited by 12
  9. 2025Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Modelscited by 11
  10. 2023Efficient GPT Model Pre-training using Tensor Train Matrix Representationcited by 8
  11. 2025Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurementscited by 8
  12. 2024The Power of Training: How Different Neural Network Setups Influence the Energy Demandcited by 7
38 more
  1. 2026A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Designcited by 5
  2. 2023ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generationcited by 4
  3. 2023Holistic chemical evaluation reveals pitfalls in reaction prediction modelscited by 4
  4. 2025Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systemscited by 4
  5. 2023CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structurescited by 3
  6. 2023AI/ML-based Load Prediction in IEEE 802.11 Enterprise Networkscited by 3
  7. 2024Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecastingcited by 3
  8. 2024AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modelingcited by 3
  9. 2024MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusioncited by 3
  10. 2023FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluationcited by 2
  11. 2024Estimating Environmental Cost Throughout Model's Adaptive Life Cyclecited by 2
  12. 2024TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservationcited by 2
  13. 2025Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networkscited by 2
  14. 2025Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solverscited by 2
  15. 2025The Hidden Cost of an Image: Quantifying the Energy Consumption of AI Image Generationcited by 2
  16. 2025GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Samplingcited by 2
  17. 2025GPT-OSS-20B: A Comprehensive Deployment-Centric Analysis of OpenAI's Open-Weight Mixture of Experts Modelcited by 2
  18. 2025Green Federated Learning via Carbon-Aware Client and Time Slot Schedulingcited by 2
  19. 2022New drugs and stock market: how to predict pharma market reaction to clinical trial announcementscited by 1
  20. 2024Distributed Learning for Wi-Fi AP Load Predictioncited by 1
  21. 2024OpenCarbonEval: A Unified Carbon Emission Estimation Framework in Large-Scale AI Modelscited by 1
  22. 2024S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Drivingcited by 1
  23. 2024Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classificationcited by 1
  24. 2024CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMscited by 1
  25. 2025How Do Companies Manage the Environmental Sustainability of AI? An Interview Study About Green AI Efforts and Regulationscited by 1
  26. 2025Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMscited by 1
  27. 2022Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cellscited by 0
  28. 2023Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexitycited by 0
  29. 2023The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trendscited by 0
  30. 2023Energy Concerns with HPC Systems and Applicationscited by 0
  31. 2024Leveraging Hybrid Intelligence Towards Sustainable and Energy-Efficient Machine Learningcited by 0
  32. 2025AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Servicescited by 0
  33. 2025Are Trees Really Green? A Detection Approach of IoT Malware Attackscited by 0
  34. 2025Toward Agentic Environments: GenAI and the Convergence of AI, Sustainability, and Human-Centric Spacescited by 0
  35. 2026AUV Trajectory Learning for Underwater Acoustic Energy Transfer and Age Minimizationcited by 0
  36. 2025Emissions and Performance Trade-off Between Small and Large Language Modelscited by 0
  37. 2026Optimising for Energy Efficiency and Performance in Machine Learningcited by 0
  38. 2026COMPASS: The explainable agentic framework for Sovereignty, Sustainability, Compliance, and Ethicscited by 0

50 most cited of 58 All 58 (JSON)