论文标题
ECO2AI：机器学习模型的碳排放跟踪是迈向可持续AI的第一步
Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI
论文作者
论文摘要
深度神经网络的规模和复杂性继续呈指数增长，大大增加了这些模型训练和推断的能源消耗。我们介绍了一个开源软件包ECO2AI，以帮助数据科学家和研究人员以直接的方式跟踪其模型的能耗和同等的二氧化碳排放。在Eco2ai中，我们强调能源消耗跟踪的准确性和正确的区域二氧化碳排放会计。我们鼓励研究社区搜索具有较低计算成本的新最佳人工智能（AI）架构。动机还来自基于AI的温室气体与可持续AI和绿色AI途径隔离周期的概念。
The size and complexity of deep neural networks continue to grow exponentially, significantly increasing energy consumption for training and inference by these models. We introduce an open-source package eco2AI to help data scientists and researchers to track energy consumption and equivalent CO2 emissions of their models in a straightforward way. In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. We encourage research community to search for new optimal Artificial Intelligence (AI) architectures with a lower computational cost. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
