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 the potential of these TinyML applications to address critical sustainability challenges. Moreover, the footprint of this emerging technology is assessed through a complete life cycle analysis of TinyML systems. From this analysis, TinyML presents opportunities to offset its 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, research directions for enabling further opportunities for TinyML to contribute to a sustainable future are outlined.

翻译：碳排放和全球废物的持续增长引发了人们对环境未来可持续性的严重担忧。日益增长的物联网可能加剧这一问题。然而，一个被称为小微机器学习的新兴领域有机会通过可持续计算实践帮助应对这些环境挑战。小微机器学习是将机器学习算法部署到低成本、低功耗微控制器系统上，实现设备端传感器分析，从而解锁众多始终在线的机器学习应用。本文讨论了这些小微机器学习应用在应对关键可持续发展挑战方面的潜力。此外，通过对小微机器学习系统进行完整的生命周期分析，评估了这项新兴技术的足迹。从该分析来看，小微机器学习通过支持减少其他行业排放的应用，提供了抵消其碳排放的机会。然而，当在全球范围内扩展时，小微机器学习系统的碳足迹不可忽视，这要求设计师在开发新设备时必须考虑环境影响。最后，概述了为进一步推动小微机器学习贡献可持续未来的研究方向。