The 2022 TinyML Design Contest yielded real-time AI algorithms for ventricular arrhythmia detection deployable on low-power microprocessors, with 41 teams successfully completing the challenge.
This paper surveys the 2022 TinyML Design Contest, highlighting methods and results for deploying AI-based ventricular arrhythmia detection on low-power microprocessors.
Artificial intelligence has achieved remarkable success in various real-world applications. However, the challenge lies in its implementation on hardware platforms with constrained resources and low power while maintaining real-time capabilities. Edge artificial intelligence, in particular, stands as a pivotal field for the practical deployment of AI. The 41st IEEE/ACM International Conference on Computer-Aided Design introduced the inaugural TinyML Design Contest in 2022. The contest entailed a rigorous, multi-month research and development competition, focusing on the creation of real-time detection algorithms for life-threatening ventricular arrhythmia. These algorithms were required to be deployable on the low-power microprocessor NUCLEO-L432KC. Open to multi-person teams worldwide, the contest garnered 150 teams participation teams from 50+ organizations, with 41 teams successfully completing the challenge. Our SEUer team secured the second place. This paper provides a detailed exposition of the contest, offering insights into its structure and objectives. Furthermore, it analyzes and discusses the methods developed by some of the entries as well as representative results. Finally, the paper concludes with directions for future improvements.
Li et al. (Mon,) conducted a review in Ventricular arrhythmia. Real-time AI detection algorithms on low-power microprocessor NUCLEO-L432KC was evaluated. The 2022 TinyML Design Contest yielded real-time AI algorithms for ventricular arrhythmia detection deployable on low-power microprocessors, with 41 teams successfully completing the challenge.