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Research papers

  • Alizzadeh et al. 2024: Analyzing the energy and accuracy of LLMs in software development.

    • Tags

      networking, network design, green software principles

    • Citation

      Alizadeh, N., Belchev, B., Saurabh, N. Kelbert, P., Castor, F. 2024. Analyzing the energy and accuracy of LLMs in software development. https://arxiv.org/pdf/2412.00329

    • Link

      https://arxiv.org/pdf/2412.00329

    • Summary

      Examines tradeoffs between model accuracy and energy consumption for LLMs.

  • Chen et al. 2023

    • Tags

      AI, frugal AI, GPT, green computing

    • Citation

      Chen, L., Zaharia, M., Zou, J. 2023. FrugalGPT: how to use large language models while reducing cost and improving performance. https://arxiv.org/pdf/2305.05176

    • Link

      https://arxiv.org/pdf/2305.05176

    • Summary

      Presents and discusses three strategies that can lower the cost of LLM inference, specifically a) adapting prompts, b) LLM approximation, c) LLM cascade. The authors present an example called FrugalGPT that uses a simple LLM cascade strategy.

  • Drouant et al. 2014

    • Tags

      networking, network design, green software principles

    • Citation

      Nicolas Drouant, Éric Rondeau, Jean- Philippe Georges, and Francis Lepage. Designing green network architectures using the ten commandments for a mature ecosystem. Computer Communications, 42:38–46, 2014

    • Link

      https://hal.science/hal-00953000

    • Summary

      Applies the "ten commandments" from ecology (specifically from Benyus, 2002: Biomimicry) to green network architecture design.

  • Faiz et al. 2024: LLMCarbon: Modeling the end-to-end carbon footprint of large language models.

    • Tags

      model, calculation, carbon footprint, tooling

    • Citation

      Faiz, A., Kaneda, S., Wang, R., Osi, R. 2024. Modeling the end-to-end carbon footprint of large language models. https://arxiv.org/html/2309.14393v2

    • Link

      https://arxiv.org/html/2309.14393v2

    • Summary

      Describes a model for estimating LLM carbon emissions.

  • Luccioni et al. 2022. Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model.

    • Tags

      BLOOM, LLM, AI, calculation, model, carbon footprint

    • Citation

      Luccioni, A.S., Viguier, S., Ligozat, A-L. 2022. Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model., https://arxiv.org/abs/2211.02001

    • Link

      https://arxiv.org/abs/2211.02001

    • Summary

      Quantifies the carbon footprint of the BLOOM model across its life cycle, with the upper estimate being ~50.5 T.

  • Patterson, et al., 2022. The carbon footprint of machine learning training will plateau then shrink

    • Tags

      carbon footprint, machine learning, AI

    • Citation

      Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia, L-M., Rothchild, D., So, D., Texier, M., Dean, J. 2022. The carbon footprint of machine learning training will plateau then shrink, https://arxiv.org/pdf/2204.05149

    • Link

      https://arxiv.org/pdf/2204.05149

    • Summary

      Describes four best poractises that can reduce energy used to train machine learning models by up to 100x and carbon emissions by 1000x.

  • Pazienza et al 2024 A holistic approach to sustainable computing

    • Tags

      carbon footprint, green computing

    • Citation

      Pazienza, A., Baselli, G., Carlo, D.C., Trussoni, M.V. 2024. A holistic approach to environmentally sustainable computing. Innovations in Systems and Software Engineering, 20: 347-371, https://link.springer.com/article/10.1007/s11334-023-00548-9

    • Link

      https://link.springer.com/article/10.1007/s11334-023-00548-9

    • Summary

      Proposes the Environmentally Sustainable Computing framework and describes use-cases.

  • Radovanović et al, 2023: Carbon-aware computing for datacenters

    • Tags

      carbon awareness, data center

    • Citation

      Koningstein R, Schneider I, Chen B, Duarte A, Roy B, et al. Carbon-aware computing for datacenters. IEEE Trans Power Syst 2023;38(2):1270–80.

    • Link

      http://dx.doi.org/10.1109/TPWRS.2022.3173250, 
https://ieeexplore.ieee.org/document/9770383.

    • Summary

      Describes Google's system for carbon-intelligent compute management. This is a system for scheduling workloads to minimize carbon footprints.

  • Riepen et al. 2002. Spatio-temporal load shifting for truly clean computing

    • Tags

      carbon awareness, load shifting, scheduling, data centers

    • Citation

      Riepen, I., Brown, T., Zavala, V.M. 2024. Spatio-temporal load shifting for truly clean computing, Advances in Applied Energy, vol 17: 100202

    • Link

      https://www.sciencedirect.com/science/article/pii/S2666792424000404?via%3Dihub#sec1

    • Summary:

      Load shifting between regions and times of day can be effective at reducing carbon emissions for compute tasks. The optimum strategy for time and space shifting varies between regions and times of year. Carbon efficiency also reduces cost - applying optimal load shifting strategies reduced compute cost by ~ 1.3 EUR/MWh.

  • Roque et al. 2024. Unveiling the Energy Vampires: A Methodology for Debugging Software Energy Consumption

    • Tags

      calculation, green computing

    • Citation

      Roque, E.B., Cruz, L., Durieux, T. 2024. Unveiling the Energy Vampires: A Methodology for Debugging Software Energy Consumption. https://arxiv.org/pdf/2412.10063

    • Link

      https://arxiv.org/pdf/2412.10063

    • Summary:

      Presents an energy debugging methodology for identifying and isolating energy consumption hotspots in software systems.

  • Tkachenko, 2024: Integrating AI’s Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector

    • Tags

      accounting, reporting, regulation, AI, carboin footprint

    • Citation

      Tkachenko, 2024: Integrating AI’s Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector, https://arxiv.org/pdf/2410.01818

    • Link

      https://arxiv.org/pdf/2410.01818

    • Summary

      Examines the integration of AI carbon emissions into risk management frameworks in banking. The paper describes how banks can identify, assess, and mitigate the carbon emissions associated with AI within their riskmanagement frameworks, including choosing energy-efficient models, using green cloud computing, and implementing lifecycle management. Advocates aligning with global standards and points out how this can ease regulatory compliance.

  • Varoquaux et al. 2024. Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI

    • Tags

      AI, carbon footprint

    • Citation

      Varoquaux, G., Luccioni, A.S., Whittaker, M. 2024. Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI. https://arxiv.org/pdf/2409.14160

    • Link:

      https://arxiv.org/pdf/2409.14160

    • Summary

      Scrutinizes the trends and trade-offs in scaling AI and refutes two common assumptions underlying the ‘bigger-is-better’ AI paradigm: 1) performance improvements result from increased scale, and 2) large-scale models are required to solve all interesting problems. The paper argues that approach is "fragile scientifically" and has negative externalities.

  • Wolff Anthony et al. 2007. CarbonTracker: tracking and predicting the carbon footprint of training deep learning models.

    • Tags

      calculation, accounting,

    • Citation

      Wolff Anthony, L.E., Kanding, B., Selvan, R. 2007. CarbonTracker: tracking and predicting the carbon footprint of training deep learning models. https://arxiv.org/pdf/2007.03051

    • Link:

      https://arxiv.org/pdf/2007.03051

    • Summary

      A tool for tracking and predicting the energy and carbon footprint of training DL models

  • Zheng et al. 2020. Mitigating curtailment and carbon emissions through load migration between data centers

    • Tags

      curtailment, load shifting, carbon awareness, data centers

    • Citation

      Zheng J, Chien AA, Suh S. Mitigating curtailment and carbon emissions through load migration between data centers. Joule 2020;4(10):2208–22.

    • Link:

      http://dx.doi.org/10.1016/j.joule.2020.08.001
https://www.sciencedirect.com/science/article/pii/S2542435120303470.

    • Summary

      Load migration can reduce renewable curtailment and GHG emissions. Existing data centers in the CAISO region can reduce up to 239 KtCO2e per year. Net abatement cost can largely stay negative

Link dump

Links that still need sorting and formatting

EPRI Powering Intelligence - data center energy consumption report 2024 https://www.epri.com/research/products/000000003002028905

Wang et al (2024) e-waste challenges of generative artificial intelligence https://www.nature.com/articles/s43588-024-00712-6?ct=t(EMAIL_CAMPAIGN_2024-NOVEMBER-08-111)?utm_medium%3Dpodcast

Hoffman and majuntke 2024 Improving Carbon Emissions of Federated Large Language Model Inference through Classification of Task-Specificity https://hotcarbon.org/assets/2024/pdf/hotcarbon24-final109.pdf

Weisner et al 2021 Let’s Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud https://arxiv.org/pdf/2110.13234Carbon-Aware Computing for Data Centers with Probabilistic Performance Guaranteesa

Hall et al 2021 Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees https://arxiv.org/pdf/2410.21510

Luccione et al 2021 ESTIMATING THE CARBON FOOTPRINT OF BLOOM, A 176B PARAMETER LANGUAGE MODEL https://arxiv.org/pdf/2211.02001

Luccione et al 2024 Power Hungry Processing: Watts Driving the Cost of AI Deployment? https://arxiv.org/pdf/2311.16863

OECD # Measuring the environmental impacts of artificial intelligence compute and applications https://www.oecd.org/en/publications/measuring-the-environmental-impacts-of-artificial-intelligence-compute-and-applications_7babf571-en.html

Bashir et al 2024 # The Climate and Sustainability Implications of Generative AI https://mit-genai.pubpub.org/pub/8ulgrckc/release/2

Li et al 2023 Making AI less thirsty - uncovering the secret water footprint of AI https://arxiv.org/abs/2304.03271

Ren et al 2024 Reconciling the contrasting narratives on the environmental impact of large language models, Nature Sci Reports https://www.nature.com/articles/s41598-024-76682-6?error=cookies_not_supported&code=1ef76dbf-5291-4fe6-8dc6-57e92d6e9550#article-info