  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.
