Abstract
This work presents an on-chip analog-to-information conversion technique that utilizes analog hyper-dimensional computing based on reservoir-computing paradigm to process electrocardiograph (ECG) signals locally in-sensor and reduce radio frequency transmission by more than three orders-of-magnitude. Instead of transmitting the naturally sparse ECG signal or extracted features, the on-chip analog-to-information converter analyzes the ECG signal through a nonlinear reservoir kernel followed by an artificial neural network, and transmits the prediction results. The proposed technique is demonstrated for detection of sepsis onset and achieves state-of-the-art accuracy and energy efficiency while reducing sensor power by 159 × with test-chips prototyped in 65 nm CMOS.
Original language | English (US) |
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Article number | 18253 |
Journal | Scientific reports |
Volume | 12 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2022 |
ASJC Scopus subject areas
- General