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

Year: 2026

Type: article

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 article 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 article concludes with directions for future improvements.

Source: ACM Transactions on Embedded Computing Systems

DOI: 10.1145/3816035

Authors: Guoqing Li, Jingwei Zhang, Meng Zhang, Tinghuan Chen, Jun Yang

Institutions: Southeast University, Chinese University of Hong Kong, Shenzhen

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province