Sustainable AI: Environmental Implications, Challenges and Opportunities

TLDR

The study investigates the environmental impact of AI’s super‑linear growth across data, algorithms, and hardware, and proposes an end‑to‑end analysis of operational and manufacturing carbon footprints to guide design and scale‑up optimizations that reduce overall emissions. The authors quantify AI’s carbon footprint by analyzing the model‑development cycle in industry‑scale machine‑learning use cases and the full life cycle of system hardware. They identify key challenges and outline development directions for lowering AI’s carbon footprint, urging the community to pursue environmentally responsible AI practices.

Abstract

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.