Green AI

TL;DR

This paper advocates for incorporating efficiency and environmental impact metrics into AI research to promote greener, more inclusive deep learning practices, addressing the rising computational costs and carbon footprint.

Contribution

It proposes making efficiency a standard evaluation criterion and reporting the financial cost of AI models to foster sustainable and accessible AI research.

Findings

Efficiency as a research metric is feasible and beneficial.

Reporting costs can guide the development of more sustainable models.

Green AI can democratize deep learning research.

Abstract

The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint [38]. Ironically, deep learning was inspired by the human brain, which is remarkably energy efficient. Moreover, the financial cost of the computations can make it difficult for academics, students, and researchers, in particular those from emerging economies, to engage in deep learning research. This position paper advocates a practical solution by making efficiency an evaluation criterion for research alongside accuracy and related measures. In addition, we propose reporting the financial cost or "price tag" of developing, training, and running models to provide baselines for the investigation of increasingly efficient methods. Our goal is to make AI both greener and more inclusive---enabling any inspired undergraduate with a laptop to write high-quality research papers. Green AI is an emerging focus at the Allen Institute for AI.