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The sustained growth of carbon emissions and global waste elicits significantsustainability concerns for our environment's future. The growing Internet ofThings (IoT) has the potential to exacerbate this issue. However, an emergingarea known as Tiny Machine Learning (TinyML) has the opportunity to helpaddress 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 thatunlocks numerous always-on ML applications. This article discusses both thepotential of these TinyML applications to address critical sustainabilitychallenges, as well as the environmental footprint of this emerging technology.Through a complete life cycle analysis (LCA), we find that TinyML systemspresent opportunities to offset their carbon emissions by enabling applicationsthat reduce the emissions of other sectors. Nevertheless, when globally scaled,the carbon footprint of TinyML systems is not negligible, necessitating thatdesigners factor in environmental impact when formulating new devices. Finally,we outline research directions to enable further sustainable contributions ofTinyML.

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