An Ultra-low Power Reconfigurable Biomedical AI Processor with Adaptive Learning for Versatile Wearable Intelligent Health Monitoring

Wearable intelligent health monitoring devices with on-device biomedical AI processor can be used to detect the abnormity in users' biomedical signals (e.g., ECG arrythmia classification, EEG-based seizure detection). This requires ultra-low power and reconfigurable biomedical AI processor to s...

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Bibliographic Details
Published in:IEEE transactions on biomedical circuits and systems 2023-10, Vol.17 (5), p.1-16
Main Authors: Liu, Jiahao, Fan, Jiajing, Zhong, Zirui, Qiu, Hui, Xiao, Jianbiao, Zhou, Yong, Zhu, Zhen, Dai, Guanghai, Wang, Ning, Liu, Qingsong, Xie, Yuxiang, Liu, Hongduo, Chang, Liang, Zhou, Jun
Format: Article
Language:English
Subjects:
Accuracy
Arrhythmias, Cardiac
Artificial Intelligence
Biomedical Processing
Classification
Data compression
EEG
EKG
Electrocardiography
Electroencephalography
Feature extraction
Gesture recognition
Humans
Learning
Microprocessors
Monitoring
Neural Network
Power consumption
Power management
Processor
Reconfigurable
Reconfiguration
Seizures
Signal Processing, Computer-Assisted
Task analysis
Telemedicine
Ultra-low Power
Wearable Electronic Devices
Wearable technology
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Summary:Wearable intelligent health monitoring devices with on-device biomedical AI processor can be used to detect the abnormity in users' biomedical signals (e.g., ECG arrythmia classification, EEG-based seizure detection). This requires ultra-low power and reconfigurable biomedical AI processor to support battery-supplied wearable devices and versatile intelligent health monitoring applications while achieving high classification accuracy. However, existing designs have issues in meeting one or more of the above requirements. In this work, a reconfigurable biomedical AI processor (named BioAIP) is proposed, mainly featuring: 1) a reconfigurable biomedical AI processing architecture to support versatile biomedical AI processing. 2) an event-driven biomedical AI processing architecture with approximate data compression to reduce the power consumption. 3) an AI-based adaptive-learning architecture to address patient-to-patient variation and improve the classification accuracy. The design has been implemented and fabricated using a 65nm CMOS process technology. It has been demonstrated with three typical biomedical AI applications, including ECG arrythmia classification, EEG-based seizure detection and EMG-based hand gesture recognition. Compared with the state-of-the-art designs optimized for single biomedical AI tasks, the BioAIP achieves the lowest energy per classification among the designs with similar accuracy, while supporting various biomedical AI tasks.
ISSN:1932-4545
1940-9990
1940-9990
DOI:10.1109/TBCAS.2023.3276782