BioAIP: A Reconfigurable Biomedical AI Processor with Adaptive Learning for Versatile Intelligent Health Monitoring

📋 论文概要

该论文提出了一款名为BioAIP的可重构生物医学AI处理器，通过自适应学习实现多功能智能健康监测。它针对神经网络分类方法在生物医学信号处理中计算复杂度高、实时性差和功耗高的问题，设计了专用加速架构。

💡 主要创新点

• 提出可重构架构，支持多种生物医学信号（如ECG、EEG）的神经网络加速，提高灵活性。
• 引入自适应学习机制，能够根据用户信号特征动态调整模型，提升分类精度和适应性。
• 通过硬件优化实现低功耗实时处理，满足可穿戴健康监测设备的严苛要求。

🏷 关键词

📄 原文摘要

health monitoring devices automatically detect abnormalities in users’ biomedical signals (e.g. arrhythmia from an ECG signal or a seizure from an EEG signal) through signal classification. Compared to conventional machine learning methods, neural-network-based AI classification methods are promising in achieving higher classification accuracy, but with significantly increased computational complexity, posing challenges to real-time performance and low power consumption. AI processors have been designed to accelerate neural networks for general AI applications such as image and voice recognition [1]. They are not suitable for biomedical AI processing, which requires a combination of biomedical and AI processing hardware. In addition,

👥 作者与机构

Jiahao Liu, Zhen Zhu, Yong Zhou, Ning Wang, Guanghai Dai, Qingsong Liu,

Jianbiao Xiao, Yuxiang Xie, Zirui Zhong, Hongduo Liu, Liang Chang, Jun Zhou University of Electronic Science and Technology of China, Chengdu, China Intelligent