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ESTIMATING THE SUSTAINABILITY OF AI MODELS BASED ON THEORETICAL MODELS AND EXPERIMENTAL DATA
  • Ralf Gitzel
Ralf Gitzel
ABB Corporate Research

Corresponding Author:[email protected]

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Abstract

As deep learning AI becomes more and more common in business and even in our daily lives, it is important to understand what the carbon impact of this type of software is. Recent papers have shown that it can be quite great, i.e., the training of a single high-end model can result in emissions of more than 500t of CO2eq. In this article we describe a life-cycle-focused framework to estimate the carbon drivers of a new deep learning model. We experimentally verify some claims in the literature and provide suggestions on how to reduce the carbon footprint of a deep learning-based offering. The article should enable developers and managers to make informed and meaningful decisions to minimize their ML projects’ sustainability impact.