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https://github.com/ejhusom/green-ai

A curated list of Green AI resources.
https://github.com/ejhusom/green-ai

List: green-ai

ai artificial-intelligence awesome awesome-list green-ai green-software machine-learning ml sustainability sustainable-ai

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A curated list of Green AI resources.

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README

        

# Green AI 🌱

A curated overview of resources for reducing the environmental footprint of AI development and usage.

Contributions and pull requests are welcome!

## Tools

### Tools for measuring and quantifying footprint

- AIPowerMeter [[Website]](https://greenai-uppa.github.io/AIPowerMeter/) [[Source code]](https://github.com/GreenAI-Uppa/AIPowerMeter)
- CarbonAI [[Source code]](https://github.com/Capgemini-Invent-France/CarbonAI)
- carbontracker [[Source code]](https://github.com/lfwa/carbontracker) [[Paper]](https://arxiv.org/pdf/2007.03051.pdf)
- CodeCarbon [[Website]](https://codecarbon.io/) [[Source code]](https://github.com/mlco2/codecarbon) [[Paper]](https://arxiv.org/pdf/1911.08354.pdf)
- Eco2AI [[Source code]](https://github.com/sb-ai-lab/Eco2AI) [[Paper]](https://arxiv.org/pdf/2208.00406.pdf)
- EcoLogits [[Source code]](https://github.com/genai-impact/ecologits) [[Documentation]](https://ecologits.ai/latest/)
- EnergyMeter [[Source code]](https://github.com/maufadel/EnergyMeter) [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10549890)
- experiment-impact-tracker [[Source code]](https://github.com/Breakend/experiment-impact-tracker) [[Paper]](https://arxiv.org/pdf/2002.05651.pdf)
- powermeter [[Source code]](https://github.com/autoai-incubator/powermeter)
- pyJoules [[Source code]](https://github.com/powerapi-ng/pyJoules)
- tracarbon [[Source code]](https://github.com/fvaleye/tracarbon)
- zeus [[Website]](https://ml.energy/zeus) [[Source code]](https://github.com/ml-energy/zeus) [[Paper]](https://www.usenix.org/system/files/nsdi23-you.pdf)

### Tools for calculation/estimation of footprint

The following tools are designed to calculate the footprint based on information about the choice of algorithms, configuration and hardware.

- Green Algorithms [[Website]](http://calculator.green-algorithms.org/) [[Paper]](https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.202100707)
- ML CO2 Impact [[Website]](https://mlco2.github.io/impact/) [[Paper]](https://arxiv.org/pdf/1910.09700.pdf)

### Tools for AI/ML development with integrated carbon footprint reporting

- d2m [[Website]](https://sintef-9012.github.io/d2m/) [[Source code]](https://github.com/SINTEF-9012/d2m) – a machine learning pipeline for ML model development with automatic monitoring and tracking of the carbon footprint

## Papers

Particularly important papers are highlighted.

- **Energy and Policy Considerations for Deep Learning in NLP** (Strubell et al. 2019) [[Paper]](https://arxiv.org/pdf/1906.02243.pdf)
- Quantifying the Carbon Emissions of Machine Learning (Lacoste et al. 2019) [[Paper]](https://arxiv.org/pdf/1910.09700.pdf)
- **Green AI** (Schwartz et al. 2020) [[Paper]](https://cacm.acm.org/magazines/2020/12/248800-green-ai/fulltext) [[Notes]](notes/schwartz2020.md)
- Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models (Anthony et al. 2020) [[Paper]](https://arxiv.org/pdf/2007.03051.pdf)
- Carbon Emissions and Large Neural Network Training (Patterson, et al. 2021) [[Paper]](https://arxiv.org/ftp/arxiv/papers/2104/2104.10350.pdf)
- Chasing Carbon: The Elusive Environmental Footprint of Computing (Gupta et al. 2020) [[Paper]](https://arxiv.org/pdf/2011.02839.pdf)
- Green Algorithms: Quantifying the Carbon Footprint of Computation (Lannelongue et al. 2021) [[Paper]](https://onlinelibrary.wiley.com/doi/10.1002/advs.202100707)
- A Pratical Guide to Quantifying Carbon Emissions for Machine Learning researchers and practitioners (Ligozat et al. 2021) [[Paper]](https://hal.archives-ouvertes.fr/hal-03376391/document)
- A framework for energy and carbon footprint analysis of distributed and federated edge learning (Savazzi et al. 2021) [[Paper]](https://arxiv.org/pdf/2103.10346.pdf) [[Notes]](notes/savazzi2021.md)
- **Aligning artificial intelligence with climate change mitigation** (Kaack et al. 2021) [[Paper]](https://hal.archives-ouvertes.fr/hal-03368037/document)
- New universal sustainability metrics to assess edge intelligence (Lenherr et al. 2021) [[Paper]](https://www.sciencedirect.com/science/article/pii/S2210537921000718?via%3Dihub)
- Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions (Ligozat et al. 2022) [[Paper]](https://arxiv.org/pdf/2110.11822.pdf)
- Measuring the Carbon Intensity of AI in Cloud Instances (Dodge et al. 2022) [[Paper]](https://arxiv.org/pdf/2206.05229.pdf)
- Estimating the Carbon Footprint of BLOOM a 176B Parameter Language Model (Luccioni et al. 2022) [[Paper]](https://arxiv.org/pdf/2211.02001.pdf)
- Bridging Fairness and Environmental Sustainability in Natural Language Processing (Hessenthaler et al. 2022) [[Paper]](https://arxiv.org/pdf/2211.04256.pdf)
- Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI (Budennyy et al. 2022) [[Paper]](https://arxiv.org/pdf/2208.00406.pdf)
- Environmental assessment of projects involving AI methods (Lefèvre et al. 2022) [[Paper]](https://hal.science/hal-03922093v1/document)
- Sustainable AI: Environmental Implications, Challenges and Opportunities (Wu et al. 2022) [[Paper]](https://arxiv.org/pdf/2111.00364.pdf)
- A first look into the carbon footprint of federated learning (Qiu et al. 2022) [[Paper]](https://arxiv.org/pdf/2102.07627.pdf) [[Notes]](notes/qiu2022.md)
- The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink (Patterson et al. 2022) [[Paper]](https://arxiv.org/ftp/arxiv/papers/2204/2204.05149.pdf)
- Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (Henderson et al. 2022) [[Paper]](https://arxiv.org/pdf/2002.05651.pdf)
- Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues (Pachot et al. 2022) [[Paper]](https://arxiv.org/ftp/arxiv/papers/2212/2212.11738.pdf)
- **Measuring the Environmental Impacts of Artificial Intelligence Compute and Applications** (OECD 2022) [[Paper]](https://www.oecd-ilibrary.org/docserver/7babf571-en.pdf?expires=1701262318&id=id&accname=guest&checksum=FAB39144A63BB5953FF7D56D7C18B147)
- Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions (Delanoë et al. 2023) [[Paper]](https://www.sciencedirect.com/science/article/pii/S030147972300049X)
- Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models (Li et al. 2023) [[Paper]](https://arxiv.org/pdf/2304.03271.pdf)
- Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training (You et al. 2023) [[Paper]](https://www.usenix.org/conference/nsdi23/presentation/you)
- Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training (Yang et al. 2023) [[Paper]](https://www.climatechange.ai/papers/iclr2023/29)
- LLMCarbon: Modeling the End-To-End Carbon Footprint of Large Language Models (Faiz et al. 2023) [[Paper]](https://arxiv.org/pdf/2309.14393.pdf)
- Power Hungry Processing: Watts Driving the Cost of AI Deployment? (Luccioni et al. 2023) [[Paper]](https://arxiv.org/pdf/2311.16863.pdf)
- A Synthesis of Green Architectural Tactics for ML-Enabled Systems (Järvenpää et al. 2023) [[Paper]](https://arxiv.org/pdf/2312.09610.pdf)
- Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems (Li et al. 2023) [[Paper]](https://arxiv.org/abs/2306.13177)
- Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study (Castaño et al. 2023) [[Paper]](https://ieeexplore.ieee.org/document/10304801)
- Estimating the environmental impact of Generative-AI services using an LCA-based methodology (Berthelot et al. 2023) [[Paper]](https://hal.univ-lorraine.fr/INRIA/hal-04346102v2)
- From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference (Samsi et al. 2023) [[Paper]](https://arxiv.org/abs/2310.03003)
- **Power Hungry Processing: Watts Driving the Cost of AI Deployment?** (Luccioni et al. 2023) [[Paper](https://arxiv.org/pdf/2311.16863.pdf)]
- Perseus: Reducing Energy Bloat in Large Model Training (Chung et al. 2024) [[Paper]](https://arxiv.org/abs/2312.06902)
- Timeshifting strategies for carbon-efficient long-running large language model training (Jagannadharao et al. 2024) [[Paper]](https://link.springer.com/article/10.1007/s11334-023-00546-x)
- Engineering Carbon Emissions Aware Machine Learning Pipelines (Husom et al. 2024) [[Paper](https://dl.acm.org/doi/pdf/10.1145/3644815.3644943)]
- Measuring and Improving the Energy Efficiency of Large Language Models Inference (Argerich et al. 2024) [[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10549890)] [[GitHub](https://github.com/maufadel/EnergyMeter)]
- Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training (Liu et al. 2024) [[Paper]](https://arxiv.org/abs/2404.01157)
- A simplified machine learning product carbon footprint evaluation tool (Lang et al.) [[Paper]](https://www.sciencedirect.com/science/article/pii/S2666789424000254)
- Beyond Efficiency: Scaling AI Sustainably (Wu et al. 2024) [[Paper]](https://arxiv.org/abs/2406.05303v1)
- Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems (Miao et al. 2024) [[Paper]](https://arxiv.org/abs/2312.15234)
- Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference (Stojkovic et al. 2024) [[Paper]](https://arxiv.org/abs/2403.20306)
- The Price of Prompting: Profiling Energy Use in Large Language Models Inference (Husom et al. 2024) [[Paper]](https://arxiv.org/abs/2407.16893)]
- Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads (Wilkins et al. 2024) [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3632775.3662830)]
- Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems (Wilkins et al. 2024) [[Paper]](https://arxiv.org/pdf/2407.04014)]
- AI, Climate, and Regulation: From Data Centers to the AI Act (Erbert et al. 2024 [[Paper]](https://arxiv.org/pdf/2410.06681)
- LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences (Fu et al. 2024) [[Paper]](https://arxiv.org/abs/2410.02950)
- Addition is all you need for energy-efficient language models (Luo et al. 2024) [[Paper]](https://arxiv.org/pdf/2410.00907)
- Artificial Intelligence in Climate Change Mitigation: A Review of Predictive Modeling and Data-Driven Solutions for Reducing Greenhouse Gas Emissions (Adegbite et al. 2024) [[Paper]](https://wjarr.com/sites/default/files/WJARR-2024-3043.pdf)

### Survey papers

- Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools (Bannour et al. 2021) [[Paper]](https://aclanthology.org/2021.sustainlp-1.2.pdf)
- **A Survey on Green Deep Learning** (Xu et al. 2021) [[Paper]](https://arxiv.org/pdf/2111.05193.pdf) [[Notes]](notes/xu2021.md)
- **A Systematic Review of Green AI** (Verdecchia et al. 2023) [[Paper]](https://arxiv.org/pdf/2301.11047.pdf)
- Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning (Luccioni et al. 2023) [[Paper]](https://arxiv.org/pdf/2302.08476v1.pdf)

## Leaderboards

- [LLM-Perf Leaderboard](https://huggingface.co/spaces/optimum/llm-perf-leaderboard)
- [ML.ENERGY Leaderboard](https://ml.energy/leaderboard/)

## Organizations, projects and foundations

- Green Software Foundation – non-profit foundation promoting software development with sustainability as a core priority [[Website]](https://greensoftware.foundation/)
- ENFIELD: European Lighthouse to Manifest Trustworthy and Green AI – project for creating a European Centre of Excellence with Green AI as one of the pillars [[Website]](https://www.enfield-project.eu/)

## Other resources

- [Awesome Green AI](https://github.com/samuelrince/awesome-green-ai/tree/main) by [samuelrince](https://github.com/samuelrince)