论文标题
朝着系统报告机器学习的能量和碳足迹
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
论文作者
论文摘要
能源和碳使用的准确报告对于理解机器学习研究的潜在气候影响至关重要。我们引入了一个框架，通过提供一个简单的接口来跟踪实时能源消耗和碳排放，并生成标准化的在线附录，从而使其更容易。利用该框架，我们为能源有效的增强学习算法创建了排行榜，以激励该领域的负责任研究作为其他机器学习领域的示例。最后，基于使用我们的框架研究的案例研究，我们提出了缓解碳排放和减少能源消耗的策略。通过使会计更轻松，我们希望进一步发展机器学习实验的可持续发展，并刺激对节能算法进行更多研究。
Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices. Utilizing this framework, we create a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research in this area as an example for other areas of machine learning. Finally, based on case studies using our framework, we propose strategies for mitigation of carbon emissions and reduction of energy consumption. By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms.
