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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)

- 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)

- **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)

- 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)

- 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)



### 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)



### Green AI and Federated Learning



- 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)

- 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)



## 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)