Power-Performance Characterization of TinyML Systems

Abstract:TinyML systems are enabling machine learning (ML) inference at the edge. However, there is little quantitative analysis of such systems. This paper presents a systematic performance and power characterization of diverse TinyML applications on microcontrollers (MCUs), spanning neural network models, software libraries, operating systems, and hardware architectures. We focus on the impact of the multiple layers of abstraction that provide higher programmability at the expense of performance and energy efficiency. We propose a model to estimate the costs of different abstraction layers and make recommendations for minimizing those costs. Our findings can help designers with Neural Architecture Search (NAS) and CNN inference optimization on edge devices.

7 pages, 9 figures. Published in Proceedings of the IEEE International Conference on Computer Design (ICCD), 2022

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2608.21646 [cs.LG]

(or arXiv:2608.21646v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.21646 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1109/ICCD56317.2022.00099 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.