A Low-Power 12-lead Arrhythmia Detection SoC Featuring a Reconfigurable CNN and Mixed-Precision Computing

作者：Yuejun Zhang, Hanyu Shi, Qikang Li, Huihong Zhang, Xinyu Li, Qingxin Xie, Zhenkai Zhou, P. Jeremy Wang · 年份：2026 · DOI：10.1109/asp-dac66049.2026.11420700 · 研究领域：Low-power high-performance VLSI design、Cardiac electrophysiology and arrhythmias、ECG Monitoring and Analysis

Cardiovascular diseases remain a leading global health threat, with arrhythmia being a key early indicator of cardiac abnormalities. The need for continuous cardiac monitoring has driven demand for portable, low-power arrhythmia detection systems. This paper presents a low-power mixed-precision System-on-Chip (SoC) solution designed for arrhythmia detection using 12-lead electrocardiogram (ECG) signals. The proposed approach employs a dynamically reconfigurable convolutional neural network (CNN) architecture with flexible hyperparameters, enhancing hardware adaptability while reducing resource overhead and power consumption. At the computation level, an 8-bit and 16-bit mixed-precision floating-point multiplier is introduced to effectively balance arithmetic accuracy and energy efficiency. Furthermore, clock gating and multi-threshold voltage techniques are employed at the digital back-end to further reduce the power consumption of the chip. Through system-level and module-level optimization, the proposed chip design is of great significance for enabling low-power arrhythmia detection in power-constrained portable medical devices.