  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.
