Green AI

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.

12 pages

Cited by in corpus (6)

• Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards
• R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method
• Towards Green Automated Machine Learning: Status Quo and Future Directions
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density
• Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast
• Energy-Efficient Parking Analytics System using Deep Reinforcement Learning