A Survey of the First TinyML@ICCAD Contest for Ventricular Arrhythmia Detection by Artificial Intelligence on Low-power Microprocessor

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.

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Publication Details

Journal
ACM Transactions on Embedded Computing Systems
Published
2026-05-18
DOI
https://doi.org/10.1145/3816035
Primary Topic
Embedded Systems Design Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

A Survey of the First TinyML@ICCAD Contest for Ventricular Arrhythmia Detection by Artificial Intelligence on Low-power Microprocessor

Tinghuan Chen, Guoqing Li, Jingwei Zhang, Meng Zhang et al.
ACM Transactions on Embedded Computing Systems
Embedded Systems Design Techniques
article

A Survey of the First TinyML@ICCAD Contest for Ventricular Arrhythmia Detection by Artificial Intelligence on Low-power Microprocessor

Tinghuan Chen, Guoqing Li, Jingwei Zhang, Meng Zhang, Jun Yang
article en

Abstract

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.

ACM Transactions on Embedded Computing Systems
Chinese University of Hong Kong, Shenzhen (CN), Innovative Research (United States) (US), Eindhoven University of Technology (NL)
Openalex Percentile: Top 3%
Embedded Systems Design Techniques
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