Shared Digital Product Passport for holistic lifecycle assessment
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article number
03654_2024_04_01
CO2 Calculation with the Product AAS
As AI models become more and more common in process industry applications, it is important to understand their carbon footprint. Recent papers have shown that it can be quite big, i.e., the training of a single high-end model can result in emissions of more than 500t of CO2eq. In this paper we discuss the factors that influence the carbon footprint of AI models, explore what impact different decisions have, and show how the footprint can be reduced. We also evaluate different models to validate or challenge theoretical assumptions from the literature. Two experimental examples using process industry data show the impact on providers of industrial analytics in particular.
| Authors | Manuel Reif, Mohammad Hossein Rimaz, Christiane Plociennik, Martin Ruskowski, Alexander David, |
|---|---|
| Publishing Date | 10 Apr 2024 |
| Format | |
| Publisher | Vulkan-Verlag GmbH |
| Language | German |
| Pages | 9 |
| Title | Shared Digital Product Passport for holistic lifecycle assessment |
| Subtitle | CO2 Calculation with the Product AAS |
| Description | As AI models become more and more common in process industry applications, it is important to understand their carbon footprint. Recent papers have shown that it can be quite big, i.e., the training of a single high-end model can result in emissions of more than 500t of CO2eq. In this paper we discuss the factors that influence the carbon footprint of AI models, explore what impact different decisions have, and show how the footprint can be reduced. We also evaluate different models to validate or challenge theoretical assumptions from the literature. Two experimental examples using process industry data show the impact on providers of industrial analytics in particular. |
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