集成电路与嵌入式系统2025，Vol.25Issue(12)：33-39,7.DOI:10.20193/j.ices2097-4191.2025.0061
基于PYNQ-Z2的机械通气后遗症预测模型嵌入式实现研究
Research on embedded implementation of mechanical ventilation sequelae prediction model based on PYNQ-Z2
JIN Ziyi 1ZHU Zhichen 2DU Jiang 3CHEN Yixiang1
作者信息
- 1. School of Software Engineering,East China Normal University,Shanghai 200062,China
- 2. China Baowu Steel Group Corporation Limited,Shanghai 201999,China
- 3. School of Medicine,Shanghai Jiaotong University,Shanghai 200025,China
- 折叠
摘要
Abstract
This paper presents the embedded deployment of the PVAC model to predict the risk of ventilator-associated complications(VAC)in patients with acute respiratory failure.The PVAC model employs the USMOTE(0.9)algorithm to address imbalanced data and integrates an AdaBoost classifier,achieving an accuracy of 71.11％and a precision of 68.89％.To overcome the limitations of existing AI medical systems that rely on cloud servers,we implemented a fully embedded deployment of the PVAC model using the PYNQ-Z2 development board.This solution offers three key advantages:offline standalone operation,hardware acceleration for improved compu-tational efficiency,and cost-effectiveness.Experimental results demonstrate that the hardware-software co-design approach significantly reduces the total execution time from 46.3 ms to 10.2 ms,achieving a speedup of 78％.Meanwhile,the ARM processors workload de-creases dramatically from 98％to 28％,with only a 0.2％drop in prediction accuracy,effectively preserving the model's original per-formance.This study not only validates the feasibility of embedding the PVAC model but also provides a reference for the localized de-ployment of other medical AI applications.Future work may focus on further optimizing the decision tree structure,leveraging the dy-namic reconfigurability of FPGAs to support more complex models,extending the capability to process temporal signals,and developing low-power modes to extend device usage time,thereby enhancing the system's practicality and applicability.关键词
嵌入式部署/软硬件协同/医疗AI/FPGA加速/异构计算Key words
embedded deployment/hardware-software co-design/medical AI/FPGA acceleration/heterogeneous computing分类
信息技术与安全科学引用本文复制引用
JIN Ziyi,ZHU Zhichen,DU Jiang,CHEN Yixiang..基于PYNQ-Z2的机械通气后遗症预测模型嵌入式实现研究[J].集成电路与嵌入式系统,2025,25(12):33-39,7.
