Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers
It addresses sustainability concerns for IoT and TinyML developers by highlighting environmental trade-offs, though it is incremental in analyzing existing technology.
The paper assesses the environmental impacts of TinyML on microcontrollers, finding that while these systems can offset carbon emissions through applications in other sectors, their global-scale footprint is significant and requires design consideration.
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.