  This paper explores the environmental impact of the super-linear growth
trends for AI from a holistic perspective, spanning Data, Algorithms, and
System Hardware. We characterize the carbon footprint of AI computing by
examining the model development cycle across industry-scale machine learning
use cases and, at the same time, considering the life cycle of system hardware.
Taking a step further, we capture the operational and manufacturing carbon
footprint of AI computing and present an end-to-end analysis for what and how
hardware-software design and at-scale optimization can help reduce the overall
carbon footprint of AI. Based on the industry experience and lessons learned,
we share the key challenges and chart out important development directions
across the many dimensions of AI. We hope the key messages and insights
presented in this paper can inspire the community to advance the field of AI in
an environmentally-responsible manner.
