Green AI Research

The widespread use of AI makes it a significant contributor to energy consumption. Hence, AI improvements will reduce its carbon footprint and environmental impacts. All of those code optimizations are also making AI greener.

Survey Papers on Green AI

Survey papers include:

• Jingjing Xu, Wangchunshu Zhou, Zhiyi Fu, Hao Zhou, Lei Li, A Survey on Green Deep Learning, Nov 2021, https://arxiv.org/abs/2111.05193 (Extensive survey paper.)
• Roberto Verdecchia, June Sallou, Luís Cruz, May 2023, A Systematic Review of Green AI, https://arxiv.org/abs/2301.11047 (Useful and broad literature review with systematic examination.)
• T Tornede, A Tornede, J Hanselle, F Mohr, 2023, Towards green automated machine learning: Status quo and future directions, Journal of Artificial Intelligence Research (JAIR), Vol. 77 (2023), https://www.jair.org/index.php/jair/article/view/14340, PDF: https://www.jair.org/index.php/jair/article/download/14340/26937
• Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini, 26 Jun 2024 (v2), The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources, https://arxiv.org/abs/2406.16746
• Aditi Singh, Nirmal Prakashbhai Patel, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei, 6 Dec 2024, A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges, https://arxiv.org/abs/2412.04782

Research Papers on Green AI

Environmental issues for AI are an active area of research:

• Tim Yarally, Luís Cruz, Daniel Feitosa, June Sallou, Arie van Deursen, March 2023, Uncovering energy-efficient practices in deep learning training: Preliminary steps towards green AI. In: International Conference on AI Engineering, Software Engineering for AI, https://arxiv.org/abs/2303.13972
• Friederike Rohde, Josephin Wagner, Andreas Meyer, Philipp Reinhard, Marcus Voss, Ulrich Petschow, June 2023, Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems, https://arxiv.org/abs/2306.13686
• BCG, Dec 2020, Top AI Experts Create CodeCarbon, a Tool to Track and Reduce Computing's CO2 Emissions, https://www.bcg.com/press/1december2020-top-ai-experts-create-codecarbon
• Harrisen Scells, Shengyao Zhuang, and Guido Zuccon. 2022. Reduce, Reuse, Recycle: Green Information Retrieval Research. In SIGIR ’22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 2825–2837. https://doi.org/10.1145/3477495.3531766, PDF: https://ielab.io/publications/pdfs/scells2022greenir.pdf
• MZ Naser, 2023, Do We Need Exotic Models? Engineering Metrics to Enable Green Machine Learning from Tackling Accuracy-Energy Trade-offs, Journal of Cleaner Production, https://www.sciencedirect.com/science/article/abs/pii/S0959652622049083
• SL Jurj, F Opritoiu, M Vladutiu, 2020, Environmentally-friendly metrics for evaluating the performance of deep learning models and systems, ICONIP 2020: Neural Information Processing, November 23–27, 2020, pp 232–244, https://link.springer.com/chapter/10.1007/978-3-030-63836-8_20
• CCJ Kuo, AM Madni, 2022, Green learning: Introduction, examples and outlook, Journal of Visual Communication and Image Representation Volume 90, February 2023, 103685, https://www.sciencedirect.com/science/article/pii/S104732032200205X
• Arun Chandrasekaran, May 2024, 3 Bold and Actionable Predictions for the Future of GenAI, Gartner, https://www.gartner.com/en/articles/3-bold-and-actionable-predictions-for-the-future-of-genai
• Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos, 9 Mar 2022 (v2), Compute Trends Across Three Eras of Machine Learning, https://arxiv.org/abs/2202.05924
• M.Tech. Suhas Shivapakash, 2024, Energy Efficient Hardware Architectures for Memory Prohibitive Deep Neural Networks, Ph.D. Thesis, der Technischen Universität Berlin, ORCID: 0000-0002-9173-213X, PDF: https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/865e682e-3d62-4d71-ae48-c79be9793962/content
• Jovan Stojkovic, Esha Choukse, Chaojie Zhang, Inigo Goiri, Josep Torrellas, 29 Mar 2024, Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference, https://arxiv.org/abs/2403.20306
• You Zhou, Xiujing Lin, Xiang Zhang, Maolin Wang, Gangwei Jiang, Huakang Lu, Yupeng Wu, Kai Zhang, Zhe Yang, Kehang Wang, Yongduo Sui, Fengwei Jia, Zuoli Tang, Yao Zhao, Hongxuan Zhang, Tiannuo Yang, Weibo Chen, Yunong Mao, Yi Li, De Bao, Yu Li, Hongrui Liao, Ting Liu, Jingwen Liu, Jinchi Guo, Xiangyu Zhao, Ying WEI, Hong Qian, Qi Liu, Xiang Wang, Wai Kin (Victor)Chan, Chenliang Li, Yusen Li, Shiyu Yang, Jining Yan, Chao Mou, Shuai Han, Wuxia Jin, Guannan Zhang, Xiaodong Zeng, Nov 2023, On the Opportunities of Green Computing: A Survey, https://arxiv.org/abs/2311.00447 (Extensive survey of environmental and green AI issues, along with a survey of various optimization methods to reduce AI resource requirements in training and inference.)
• Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, Carl Yang, Yue Cheng, Liang Zhao, 4 Jan 2024, Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models https://arxiv.org/abs/2401.00625 (A general survey paper with coverage of many techniques including this one.)
• MAURICIO FADEL ARGERICH, MARTA PATIÑO-MARTÍNEZ, 2024, Measuring and Improving the Energy Efficiency of Large Language Models Inference, IEEE Access, https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10549890
• Cell, 2023, The growing energy footprint of artificial intelligence, Cell, Volume 7, Issue 10, Pages 2191-2194, October 18, 2023, https://www.cell.com/joule/abstract/S2542-4351(23)00365-3
• James Vincent, Feb 17, 2024, How much electricity does AI consume? https://www.theverge.com/24066646/ai-electricity-energy-watts-generative-consumption
• Alex de Vries, Oct 13, 2023 , The AI Boom Could Use a Shocking Amount of Electricity, Scientific American, https://www.scientificamerican.com/article/the-ai-boom-could-use-a-shocking-amount-of-electricity/
• Enrico Barbierato, Alice Gatti, 20 February 2024, Toward Green AI: A Methodological Survey of the Scientific Literature, PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10418137
• Minghao Yan, Hongyi Wang, Shivaram Venkataraman, 9 Jan 2024 (v2), PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices, https://arxiv.org/abs/2310.19991 (Faster inference with a focus on pipelining and scheduling of hardware acceleration.)
• Yuyi Mao, Xianghao Yu, Kaibin Huang, Ying-Jun Angela Zhang, Jun Zhang, Dec 2023, Green Edge AI: A Contemporary Survey, https://arxiv.org/abs/2312.00333
• Q Liang, WA Hanafy, N Bashir, D Irwin, P Shenoy, 2023, Energy Time Fairness: Balancing Fair Allocation of Energy and Time for GPU Workloads, https://lass.cs.umass.edu/papers/pdf/sec2023-energytimefairness.pdf
• Grant Wilkins, 3 June 202, Online Workload Allocation and Energy Optimization in Large Language Model Inference Systems, Master of Philosophy in Advanced Computer Science, Churchill College, University of Cambridge, https://grantwilkins.github.io/gfw27_project.pdf
• D Wright, C Igel, G Samuel, R Selvan, 2023 Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI, https://arxiv.org/pdf/2309.02065.pdf
• Lukas Arno Jakob Cavigelli, Qiuting Huang, et al., Jul 26, 2019, Towards Energy-Efficient Convolutional Neural Network Inference, https://www.amazon.com/Towards-Energy-Efficient-Convolutional-Network-Inference/dp/3866286511/
• Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu, 16 Jan 2024, A Survey of Resource-efficient LLM and Multimodal Foundation Models, https://arxiv.org/abs/2401.08092 Project: https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey
• Emily Cerf, June 20, 2024, Researchers run high-performing large language model on the energy needed to power a lightbulb, https://news.ucsc.edu/2024/06/matmul-free-llm.html
• Benj Edwards, 26 June, 2024, Researchers upend AI status quo by eliminating matrix multiplication in LLMs, https://arstechnica.com/information-technology/2024/06/researchers-upend-ai-status-quo-by-eliminating-matrix-multiplication-in-llms/
• Ignacio de Gregorio Noblejas, June 22, 2024, AI's Elephant in the Room: Energy Constraints, https://thetechoasis.beehiiv.com/p/ais-elephant-room-energy-constraints
• Yehia Ibrahim Alzoubi, Alok Mishra, 25 August 2024, Green artificial intelligence initiatives: Potentials and challenges, Journal of Cleaner Production Volume 468, 143090, https://www.sciencedirect.com/science/article/pii/S0959652624025393
• Grant Wilkins, Srinivasan Keshav, Richard Mortier, 4 Jul 2024, Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems, https://arxiv.org/abs/2407.04014
• 18 Apr 2024 (v2), The Efficiency Spectrum of Large Language Models: An Algorithmic Survey, Tianyu Ding, Tianyi Chen, Haidong Zhu, Jiachen Jiang, Yiqi Zhong, Jinxin Zhou, Guangzhi Wang, Zhihui Zhu, Ilya Zharkov, Luming Liang, https://arxiv.org/abs/2312.00678
• Guanqiao Qu, Qiyuan Chen, Wei Wei, Zheng Lin, Xianhao Chen, Kaibin Huang, July 2024, Mobile Edge Intelligence for Large Language Models: A Contemporary Survey, https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.172115025.57884352
• Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri, Josep Torrellas, Esha Choukse, 1 Aug 2024, DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency, https://arxiv.org/abs/2408.00741
• Paula Rooney, 19 Aug 2024, AI to go nuclear? Data center deals say it’s inevitable, CIO, https://www.cio.com/article/3487339/ai-to-go-nuclear-data-center-deals-say-its-inevitable.html
• Melody Petersen, Aug. 26, 2024, How much more water and power does AI computing demand? Tech firms don’t want you to know, https://www.latimes.com/environment/story/2024-08-26/tech-firms-conceal-water-and-power-demands-of-ai-computing
• Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren, 29 Oct 2023 (v3), Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models, https://arxiv.org/abs/2304.03271 https://github.com/Ren-Research/Making-AI-Less-Thirsty
• Alex de Vries, 2023, The growing energy footprint of artificial intelligence, Joule, Volume 7, Issue 10, Pages 2191-2194, ISSN 2542-4351, https://doi.org/10.1016/j.joule.2023.09.004 https://www.sciencedirect.com/science/article/abs/pii/S2542435123003653
• Lora Kolodny, Aug 28 2024 Elon Musk’s xAI accused of worsening Memphis smog with unauthorized gas turbines at data center, https://www.cnbc.com/2024/08/28/musk-xai-accused-of-worsening-memphis-smog-with-unauthorized-turbines.html
• Dustin Wright, Christian Igel, Gabrielle Samuel, Raghavendra Selvan, 5 Sep 2023, Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI, https://arxiv.org/abs/2309.02065
• Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini, 26 Jun 2024 (v2), The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources, https://arxiv.org/abs/2406.16746
• Matt Egan, September 12, 2024, Mr ChatGPT and other AI power players are going to the White House to discuss AI’s massive thirst for energy, https://edition.cnn.com/2024/09/12/tech/ai-chatgpt-white-house-power-energy/index.html
• Yuzhuo Li, Mariam Mughees, Yize Chen, Yunwei Ryan Li, 9 Sep 2024, The Unseen AI Disruptions for Power Grids: LLM-Induced Transients, https://arxiv.org/abs/2409.11416
• Pranshu Verma and Shelly Tan, September 18, 2024. A bottle of water per email: the hidden environmental costs of using AI chatbots. AI bots generate a lot of heat, and keeping their computer servers running exacts a toll, https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/
• Douglas C. Youvan, September 27, 2024, Building and Running Large-Scale Language Models: The Infrastructure and Techniques Behind GPT-4 , https://www.researchgate.net/profile/Douglas-Youvan/publication/384398902_Building_and_Running_Large-Scale_Language_Models_The_Infrastructure_and_Techniques_Behind_GPT-4/links/66f6f4d3906bca2ac3d20e68/Building-and-Running-Large-Scale-Language-Models-The-Infrastructure-and-Techniques-Behind-GPT-4.pdf
• Kai Ebert, Nicolas Alder, Ralf Herbrich, Philipp Hacker, 9 Oct 2024, AI, Climate, and Regulation: From Data Centers to the AI Act, https://arxiv.org/abs/2410.06681
• Yue Zheng, Yuhao Chen, Bin Qian, Xiufang Shi, Yuanchao Shu, Jiming Chen, 29 Sep 2024, A Review on Edge Large Language Models: Design, Execution, and Applications, https://arxiv.org/abs/2410.11845
• Mikael Wiberg, Robin Teigland, Oct 2024, Computing for the 22nd Century, More-than-human to see the environmental footprints of profound technologies, HTTF ’24, October 21–23, 2024, Santa Cruz, CA, USA, https://dl.acm.org/doi/pdf/10.1145/3686169.3686177
• D.Breen, Oct 2024, Towards Sustainable CNNs: Tensor Decompositions for Green AI Solutions: Exploring Energy Consumption of Large CNNs, Master's Thesis, Systems and Control & Robotics, Delft University of Technology, https://repository.tudelft.nl/file/File_8208301f-51ef-4edf-bd12-d6ec3d5a8711
• Baolin Li, April 2024, Making Machine Learning on HPC Systems Cost-Effective and Carbon-Friendly, Ph.D. Thesis, The Department of Electrical and Computer Engineering, Computer Engineering, Northeastern University, Boston, Massachusetts, https://repository.library.northeastern.edu/files/neu:4f248m902/fulltext.pdf
• Ren, S., Tomlinson, B., Black, R.W. et al. Reconciling the contrasting narratives on the environmental impact of large language models. Sci Rep 14, 26310 (2024). https://doi.org/10.1038/s41598-024-76682-6 https://www.nature.com/articles/s41598-024-76682-6 https://www.nature.com/articles/s41598-024-76682-6.pdf
• I Haider, TT Bhatti, 2024. The Environmental Cost of Artificial Intelligence, Focus, https://irs.org.pk/Focus/FOct24.pdf
• Lynn Greiner, Dec 02, 2024, Data centers go nuclear for power-hungry AI workloads, https://www.networkworld.com/article/3613868/data-centers-go-nuclear-for-power-hungry-ai-workloads.html
• Meta, December 3, 2024, Accelerating the Next Wave of Nuclear to Power AI Innovation, https://sustainability.atmeta.com/blog/2024/12/03/accelerating-the-next-wave-of-nuclear-to-power-ai-innovation/
• Aditi Singh, Nirmal Prakashbhai Patel, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei, 6 Dec 2024, A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges, https://arxiv.org/abs/2412.04782
• Yuelin Han, Zhifeng Wu, Pengfei Li, Adam Wierman, Shaolei Ren, 9 Dec 2024, The Unpaid Toll: Quantifying the Public Health Impact of AI, https://arxiv.org/abs/2412.06288

Energy Efficient Research

Energy efficiency is an important part of green AI, perhaps the most important (there's also water). A lot of AI optimization research is also relevant to reducing energy usage. Here are some paper specifically on energy:

• Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri, Josep Torrellas, Esha Choukse, 1 Aug 2024, DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency, https://arxiv.org/abs/2408.00741
• Andreas Kosmas Kakolyris, Dimosthenis Masouros, Petros Vavaroutsos, Sotirios Xydis, Dimitrios Soudris, 5 Aug 2024, SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving, https://arxiv.org/abs/2408.05235
• Lukas Arno Jakob Cavigelli, Qiuting Huang, et al., Jul 26, 2019, Towards Energy-Efficient Convolutional Neural Network Inference, https://www.amazon.com/Towards-Energy-Efficient-Convolutional-Network-Inference/dp/3866286511/
• Tim Yarally, Luís Cruz, Daniel Feitosa, June Sallou, Arie van Deursen, March 2023, Uncovering energy-efficient practices in deep learning training: Preliminary steps towards green AI. In: International Conference on AI Engineering, Software Engineering for AI, https://arxiv.org/abs/2303.13972
• Sheila Chiang, Aug 26 2024, This Nvidia partner says it can cut data center energy use by 50% as AI boom strains power grid, https://www.cnbc.com/2024/08/27/nvidia-partner-sustainable-metal-cloud-ai-data-center-energy-consumption.html
• CNBC, Jun 12 2024, Data center liquid cooling is accelerating and it’s accelerating now, says Vertiv CEO, https://www.cnbc.com/video/2024/06/11/data-center-liquid-cooling-is-accelerating-and-its-accelerating-now-says-vertiv-ceo.html
• Dustin Wright, Christian Igel, Gabrielle Samuel, Raghavendra Selvan, 5 Sep 2023, Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI, https://arxiv.org/abs/2309.02065
• Yuzhuo Li, Mariam Mughees, Yize Chen, Yunwei Ryan Li, 9 Sep 2024, The Unseen AI Disruptions for Power Grids: LLM-Induced Transients, https://arxiv.org/abs/2409.11416
• Theo Gregersen, Pratyush Patel, Esha Choukse, 26 Sep 2024, Input-Dependent Power Usage in GPUs, https://arxiv.org/abs/2409.18324
• Michelle Horton, Oct 16, 2024, Maximizing Energy and Power Efficiency in Applications with NVIDIA GPUs, https://developer.nvidia.com/blog/maximizing-energy-and-power-efficiency-in-applications-with-nvidia-gpus/
• Minghao Yan, Hongyi Wang, Shivaram Venkataraman, 9 Jan 2024 (v2), PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices, https://arxiv.org/abs/2310.19991
• D.Breen, Oct 2024, Towards Sustainable CNNs: Tensor Decompositions for Green AI Solutions: Exploring Energy Consumption of Large CNNs, Master's Thesis, Systems and Control & Robotics, Delft University of Technology, https://repository.tudelft.nl/file/File_8208301f-51ef-4edf-bd12-d6ec3d5a8711
• Baolin Li, April 2024, Making Machine Learning on HPC Systems Cost-Effective and Carbon-Friendly, Ph.D. Thesis, The Department of Electrical and Computer Engineering, Computer Engineering, Northeastern University, Boston, Massachusetts, https://repository.library.northeastern.edu/files/neu:4f248m902/fulltext.pdf
• Grant Wilkins, Srinivasan Keshav, Richard Mortier, 4 Jul 2024, Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems, https://arxiv.org/abs/2407.04014
• Robert Corwin Nov 2024, Running Large Language Models Privately: A comparison of frameworks, models, and costs, https://towardsdatascience.com/running-large-language-models-privately-a-comparison-of-frameworks-models-and-costs-ac33cfe3a462
• Rei Barjami, Antonio Miele, and Luca Mottola. 2024. Intermittent Inference: Trading a 1% Accuracy Loss for a 1.9x Throughput Speedup. In Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems (SenSys '24). Association for Computing Machinery, New York, NY, USA, 647–660. https://doi.org/10.1145/3666025.3699364 https://dl.acm.org/doi/abs/10.1145/3666025.3699364 https://dl.acm.org/doi/pdf/10.1145/3666025.3699364
• R. Geens, M. Shi, A. Symons, C. Fang and M. Verhelst, "Energy Cost Modelling for Optimizing Large Language Model Inference on Hardware Accelerators," 2024 IEEE 37th International System-on-Chip Conference (SOCC), Dresden, Germany, 2024, pp. 1-6, doi: 10.1109/SOCC62300.2024.10737844. https://ieeexplore.ieee.org/abstract/document/10737844/

Water Usage of AI

Research papers on water consumption of AI include:

• Melody Petersen, Aug. 26, 2024, How much more water and power does AI computing demand? Tech firms don’t want you to know, https://www.latimes.com/environment/story/2024-08-26/tech-firms-conceal-water-and-power-demands-of-ai-computing
• Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren, 29 Oct 2023 (v3), Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models, https://arxiv.org/abs/2304.03271 https://github.com/Ren-Research/Making-AI-Less-Thirsty
• A. Shaji George, A. S. Hovan George, A. S. Gabrio Martin, 2023, The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT, https://doi.org/10.5281/zenodo.7855594 https://puiij.com/index.php/research/article/view/39 https://puiij.com/index.php/research/article/download/39/23
• Dustin Wright, Christian Igel, Gabrielle Samuel, Raghavendra Selvan, 5 Sep 2023, Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI, https://arxiv.org/abs/2309.02065
• Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini, 26 Jun 2024 (v2), The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources, https://arxiv.org/abs/2406.16746
• Pranshu Verma and Shelly Tan, September 18, 2024. A bottle of water per email: the hidden environmental costs of using AI chatbots. AI bots generate a lot of heat, and keeping their computer servers running exacts a toll, https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/
• I Haider, TT Bhatti, 2024. The Environmental Cost of Artificial Intelligence, Focus, https://irs.org.pk/Focus/FOct24.pdf
• Noah Shumba, Opelo Tshekiso, Pengfei Li, Giulia Fanti, Shaolei Ren, 4 Dec 2024, A Water Efficiency Dataset for African Data Centers, https://arxiv.org/abs/2412.03716 https://huggingface.co/datasets/masterlion/WaterEfficientDatasetForAfricanCountries/tree/main

More AI Research

Read more about:

• Safety of AI
• GenAI Market
• Copyright issues
• Inference Optimizations
• « Research Home