The proliferation of software and AI comes with a hidden risk: its growing energy and carbon footprint. As concerns regarding environmental sustainability come to the forefront, understanding and optimizing how software impacts the environment becomes paramount. In this paper, we present a state-of-the-art review of methods and tools that enable the measurement of software and AI-related energy and/or carbon emissions. We introduce a taxonomy to categorize the existing work as monitoring, estimation, or black-box approaches. We delve deeper into the tools and compare them across different dimensions and granularity - for example, whether their measurement encompasses energy and carbon emissions and the components considered (like CPU, GPU, RAM, etc). We present our observations on the practical use (component wise consolidation of approaches) as well as the challenges that we have identified across the current state-of-the-art. As we start an initiative to address these challenges, we emphasize active collaboration across the community in this important field.
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11:00 - 12:30
Session 2: Pitch Session 2 (7-minute pitch of each paper and 3-minute question/comment)GREENS at 203 Chair(s): Elisa Yumi Nakagawa University of São Paulo
Angelika Kaplan Karlsruhe Institute of Technology (KIT), Jan Keim Karlsruhe Institute of Technology (KIT), Lukas Greiner Karlsruhe Institute of Technology (KIT), Ralf Sieger FZI Research Center for Information Technology, Raffaela Mirandola Karlsruhe Institute of Technology (KIT), Ralf Reussner Karlsruhe Institute of Technology (KIT) and FZI - Research Center for Information Technology (FZI)