摘要: Background: The rise of machine learning (ML) systems has exacerbated their carbon footprint due to increased capabilities and model sizes. However, there is scarce knowledge on how the carbon footprint of ML models is actually me... 展开
作者 | Joel Castaño Silverio Martínez-Fernández Xavier Franch Justus Bogner | ||
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作者单位 | |||
文集名称 | 2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement | ||
出版年 | 2023 | ||
会议名称 | ACM/IEEE International Symposium on Empirical Software Engineering and Measurement | ||
页码 | 1-12 | 开始页/总页数 | 1 / 12 |
会议地点 | New Orleans(US) | 会议年 | 2023 |
关键词 | Training Correlation Green products Carbon dioxide Energy efficiency Software measurement Data mining | ||
馆藏号 | IEL35260 (10304838) |