In-sensor neural network for high energy efficiency analog-to-information conversion
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 $$159\times $$ 159 × with test-chips prototyped in 65 nm CMOS.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 29, 2022
- Source ID
- 10.1038/s41598-022-23100-4
Entities
People
- Arindam Sanyal
- Imon Banerjee
- Sudarsan Sadasivuni
- Sumukh Prashant Bhanushali
Organizations
- Air Force Research Laboratory
- National Science Foundation