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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2407.10237 |
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| _version_ | 1866929420240945152 |
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| author | Clemm, Christian Stobbe, Lutz Wimalawarne, Kishan Druschke, Jan |
| author_facet | Clemm, Christian Stobbe, Lutz Wimalawarne, Kishan Druschke, Jan |
| contents | The immense technological progress in artificial intelligence research and applications is increasingly drawing attention to the environmental sustainability of such systems, a field that has been termed Green AI. With this contribution we aim to broaden the discourse on Green AI by investigating the current status of approaches to both environmental assessment and ecodesign of AI systems. We propose a life-cycle-based system thinking approach that accounts for the four key elements of these software-hardware-systems: model, data, server, and cloud. We conduct an exemplary estimation of the carbon footprint of relevant compute hardware and highlight the need to further investigate methods for Green AI and ways to facilitate wide-spread adoption of its principles. We envision that AI could be leveraged to mitigate its own environmental challenges, which we denote as AI4greenAI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10237 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Green AI: Current status and future research Clemm, Christian Stobbe, Lutz Wimalawarne, Kishan Druschke, Jan Computers and Society Artificial Intelligence Hardware Architecture The immense technological progress in artificial intelligence research and applications is increasingly drawing attention to the environmental sustainability of such systems, a field that has been termed Green AI. With this contribution we aim to broaden the discourse on Green AI by investigating the current status of approaches to both environmental assessment and ecodesign of AI systems. We propose a life-cycle-based system thinking approach that accounts for the four key elements of these software-hardware-systems: model, data, server, and cloud. We conduct an exemplary estimation of the carbon footprint of relevant compute hardware and highlight the need to further investigate methods for Green AI and ways to facilitate wide-spread adoption of its principles. We envision that AI could be leveraged to mitigate its own environmental challenges, which we denote as AI4greenAI. |
| title | Towards Green AI: Current status and future research |
| topic | Computers and Society Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2407.10237 |