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.

翻译：碳排放和全球废弃物的持续增长引发了对环境未来的重大可持续性担忧。日益发展的物联网（IoT）可能加剧这一问题。然而，一个新兴领域——微型机器学习（TinyML）——有望通过可持续计算实践帮助应对这些环境挑战。TinyML将机器学习（ML）算法部署到低成本、低功耗的微控制器系统上，实现设备端传感器分析，从而解锁众多常开型ML应用。本文探讨了TinyML应用在应对关键可持续性挑战方面的潜力，以及这一新兴技术的环境足迹。通过完整的生命周期分析（LCA），我们发现TinyML系统可通过支持减少其他行业排放的应用来抵消自身碳排放，从而提供减排机会。然而，在全球大规模部署时，TinyML系统的碳足迹不容忽视，这要求设计者在开发新设备时必须将环境影响纳入考量。最后，我们提出了进一步增强TinyML可持续贡献的研究方向。