Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers

TL;DR

TinyML offers promising sustainable applications by enabling on-device analytics, but its environmental impact must be carefully managed as its global scale could contribute significantly to carbon emissions.

Contribution

This paper provides a comprehensive life cycle analysis of TinyML, highlighting its potential to reduce emissions and the need to consider its environmental footprint at scale.

Findings

TinyML can offset emissions by enabling sustainable applications.

The global scale of TinyML could lead to a non-negligible carbon footprint.

Designers should incorporate environmental impact assessments in TinyML development.

Abstract

The sustained growth of carbon emissions and global waste elicits significant sustainability concerns for our environment's future. The growing Internet of Things (IoT) has the potential to exacerbate this issue. However, an emerging area known as Tiny Machine Learning (TinyML) has the opportunity to help address these environmental challenges through sustainable computing practices. TinyML, the deployment of machine learning (ML) algorithms onto low-cost, low-power microcontroller systems, enables on-device sensor analytics that unlocks numerous always-on ML applications. This article discusses both the potential of these TinyML applications to address critical sustainability challenges, as well as the environmental footprint of this emerging technology. Through a complete life cycle analysis (LCA), we find that TinyML systems present opportunities to offset their carbon emissions by enabling applications that reduce the emissions of other sectors. Nevertheless, when globally scaled, the carbon footprint of TinyML systems is not negligible, necessitating that designers factor in environmental impact when formulating new devices. Finally, we outline research directions to enable further sustainable contributions of TinyML.