mlsanityData to 14 Oct 2026

Green AI

arXiv 1907.10597 ↗First posted 22 Jul 2019cs.CY cs.CL cs.CVcited by 627

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Abstract (arXiv)

The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint [38]. Ironically, deep learning was inspired by the human brain, which is remarkably energy efficient. Moreover, the financial cost of the computations can make it difficult for academics, students, and researchers, in particular those from emerging economies, to engage in deep learning research. This position paper advocates a practical solution by making efficiency an evaluation criterion for research alongside accuracy and related measures. In addition, we propose reporting the financial cost or "price tag" of developing, training, and running models to provide baselines for the investigation of increasingly efficient methods. Our goal is to make AI both greener and more inclusive---enabling any inspired undergraduate with a laptop to write high-quality research papers. Green AI is an emerging focus at the Allen Institute for AI.

Key facts. Cited by 627 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jul 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 93; 2022: 107; 2023: 121; 2024: 83; 2025: 103; 2026: 120.

627 papers.

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  1. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  2. 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 713
  3. 2022Mixture-of-Experts with Expert Choice Routingcited by 623
  4. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  5. 2022Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Spacecited by 532
  6. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
  7. 2022Confident Adaptive Language Modelingcited by 310
  8. 2021GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Trainingcited by 274
  9. 2023Challenges and Applications of Large Language Modelscited by 254
  10. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  11. 2023TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddingscited by 202
  12. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 184
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  1. 2021Efficient Large Scale Language Modeling with Mixtures of Expertscited by 175
  2. 2024AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?cited by 158
  3. 2021Self-Supervised Representation Learning: Introduction, Advances and Challengescited by 148
  4. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 148
  5. 2022Towards a Cleaner Document-Oriented Multilingual Crawled Corpuscited by 143
  6. 2021An Empirical Investigation of the Role of Pre-training in Lifelong Learningcited by 134
  7. 2021Avalanche: an End-to-End Library for Continual Learningcited by 124
  8. 2023GEO-Bench: Toward Foundation Models for Earth Monitoringcited by 124
  9. 2023Generative AIcited by 120
  10. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 111
  11. 2021UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmarkcited by 98
  12. 2021SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Eventscited by 84
  13. 2021Sparse Training via Boosting Pruning Plasticity with Neuroregenerationcited by 84
  14. 2021Top-KAST: Top-K Always Sparse Trainingcited by 79
  15. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78
  16. 2023A Systematic Review of Green AIcited by 77
  17. 2023Efficient Benchmarking of Language Modelscited by 71
  18. 2024A Complete Survey on LLM-based AI Chatbotscited by 71
  19. 2021bert2BERT: Towards Reusable Pretrained Language Modelscited by 70
  20. 2022ELLE: Efficient Lifelong Pre-training for Emerging Datacited by 70
  21. 2021CCVS: Context-aware Controllable Video Synthesiscited by 69
  22. 2021FLEX: Unifying Evaluation for Few-Shot NLPcited by 67
  23. 2022One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesiacited by 66
  24. 2021YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimizationcited by 62
  25. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 61
  26. 2022Adaptive Second Order Coresets for Data-efficient Machine Learningcited by 60
  27. 2023Continual Learning: Applications and the Road Forwardcited by 59
  28. 2023On the Impact of Calibration Data in Post-training Quantization and Pruningcited by 58
  29. 2022EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruningcited by 56
  30. 2025Energy Considerations of Large Language Model Inference and Efficiency Optimizationscited by 55
  31. 2022Green Hierarchical Vision Transformer for Masked Image Modelingcited by 50
  32. 2022Staged Training for Transformer Language Modelscited by 49
  33. 2022Evaluation Gaps in Machine Learning Practicecited by 49
  34. 2024CiteME: Can Language Models Accurately Cite Scientific Claims?cited by 48
  35. 2021Towards General Purpose Vision Systemscited by 46
  36. 2023Revisiting Scene Text Recognition: A Data Perspectivecited by 45
  37. 2023Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Modelscited by 44
  38. 2023Towards Sustainable Learning: Coresets for Data-efficient Deep Learningcited by 44

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