Sustainable AI: Environmental Implications, Challenges and Opportunities

TL;DR

This paper provides a comprehensive analysis of AI's environmental impact, examining the carbon footprint across data, algorithms, and hardware, and proposing strategies for sustainable AI development.

Contribution

It offers an end-to-end assessment of AI's environmental footprint and discusses hardware-software design strategies to reduce carbon emissions.

Findings

AI's carbon footprint is significant across development and operation phases

Hardware and software optimization can substantially reduce AI's environmental impact

Industry insights highlight key challenges and future directions for sustainable AI

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.