Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning
This addresses the environmental impact of ML for researchers and practitioners, but it is incremental as it extends prior limited analyses with a broader survey.
The study surveyed carbon emissions from 95 machine learning models in NLP and computer vision, analyzing emissions over time and in relation to model performance, and proposed a centralized repository for tracking emissions.
Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. Existing research on the environmental impacts of ML has been limited to analyses covering a small number of models and does not adequately represent the diversity of ML models and tasks. In the current study, we present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision. We analyze them in terms of the energy sources used, the amount of CO2 emissions produced, how these emissions evolve across time and how they relate to model performance. We conclude with a discussion regarding the carbon footprint of our field and propose the creation of a centralized repository for reporting and tracking these emissions.