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

arXiv: https://arxiv.org/abs/2301.11899 · HTML: https://mlsanity.com/paper/2301.11899/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems](https://mlsanity.com/paper/1810.07751/) (2018)
- [Edge Impulse: An MLOps Platform for Tiny Machine Learning](https://mlsanity.com/paper/2212.03332/) (2022)
- [Widening Access to Applied Machine Learning with TinyML](https://mlsanity.com/paper/2106.04008/) (2021)
- [Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots](https://mlsanity.com/paper/2205.05748/) (2022)
- [Assessing the embodied carbon footprint of IoT edge devices with a bottom-up life-cycle approach](https://mlsanity.com/paper/2105.02082/) (2021)

## Built on by (most cited first, 4 of 4)

- [MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI](https://mlsanity.com/paper/2410.12032/) (2024)
- [Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race](https://mlsanity.com/paper/2502.20016/) (2025)
- [Datasheets for Machine Learning Sensors](https://mlsanity.com/paper/2306.08848/) (2023)
- [Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge](https://mlsanity.com/paper/2509.08193/) (2025)

## Abstract (arXiv)

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.