The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multi-month, research and development competition. TDC'22 focuses on real-world medical problems that require the innovation and implementation of artificial intelligence/machine learning (AI/ML) algorithms on implantable devices. The challenge problem of TDC'22 is to develop a novel AI/ML-based real-time detection algorithm for life-threatening ventricular arrhythmia over low-power microcontrollers utilized in Implantable Cardioverter-Defibrillators (ICDs). The dataset contains more than 38,000 5-second intracardiac electrograms (IEGMs) segments over 8 different types of rhythm from 90 subjects. The dedicated hardware platform is NUCLEO-L432KC manufactured by STMicroelectronics. TDC'22, which is open to multi-person teams world-wide, attracted more than 150 teams from over 50 organizations. This paper first presents the medical problem, dataset, and evaluation procedure in detail. It further demonstrates and discusses the designs developed by the leading teams as well as representative results. This paper concludes with the direction of improvement for the future TinyML design for health monitoring applications.

翻译：首届ACM/IEEE TinyML设计竞赛（TDC）于2022年在第41届国际计算机辅助设计会议（ICCAD）上举办，是一项为期数月的高难度研发竞赛。TDC'22聚焦于需要创新并实现植入式设备上人工智能/机器学习（AI/ML）算法的真实医疗问题。其挑战性任务是在植入式心律转复除颤器（ICD）中使用的低功耗微控制器上，开发基于AI/ML的实时检测算法，以识别危及生命的室性心律失常。数据集包含来自90名受试者、超过38,000个5秒心内电图（IEGM）片段，涵盖8种不同类型的心律。专用硬件平台为意法半导体生产的NUCLEO-L432KC。面向全球多人团队开放的TDC'22吸引了来自50多个组织的150余支队伍。本文首先详细阐述了医疗问题、数据集及评估流程，进而展示并讨论了领先团队所开发的设计方案及代表性结果。最后，本文为未来面向健康监测应用的TinyML设计指明了改进方向。