TY - GEN
T1 - Multi-Task Learning Mixed-Signal Classifier for In-situ Detection of Atrial Fibrillation and Sepsis
AU - Sadasivuni, Sudarsan
AU - Bhanushali, Sumukh Prashant
AU - Singamsetti, Sai Srinivasa
AU - Banerjee, Imon
AU - Sanyal, Arindam
N1 - Funding Information:
ACKNOWLEDGMENT This material is based on research sponsored by Air Force Research Laboratory under agreement number FA8650-18-2-5402. The U.S. Government is authorized to reproduce and
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper presents an on-chip analog machine learning (ML) classifier IC for detecting atrial fibrillation (AFib) and sepsis from electrocardiogram (ECG) signal. The proposed technique allows continuous in-situ health surveillance using wearables with embedded AI for early detection of underlying health issues. The analog classifier uses custom activation function and performs in-memory computation (IMC) with switched-capacitor circuits for reduced data movement. Designed in 65nm, the test chip achieves average accuracy of 98.2% for AFib detection, and 90.7% for predicting sepsis 4 hours before onset. The energy efficiency of the test-chip is 12.9nJ/classification which is 4× better than state-of-the-art.
AB - This paper presents an on-chip analog machine learning (ML) classifier IC for detecting atrial fibrillation (AFib) and sepsis from electrocardiogram (ECG) signal. The proposed technique allows continuous in-situ health surveillance using wearables with embedded AI for early detection of underlying health issues. The analog classifier uses custom activation function and performs in-memory computation (IMC) with switched-capacitor circuits for reduced data movement. Designed in 65nm, the test chip achieves average accuracy of 98.2% for AFib detection, and 90.7% for predicting sepsis 4 hours before onset. The energy efficiency of the test-chip is 12.9nJ/classification which is 4× better than state-of-the-art.
KW - Machine learning
KW - atrial fibrillation
KW - mixed-signal classifier and in-memory computation
KW - sepsis
UR - http://www.scopus.com/inward/record.url?scp=85124202271&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124202271&partnerID=8YFLogxK
U2 - 10.1109/BioCAS49922.2021.9644994
DO - 10.1109/BioCAS49922.2021.9644994
M3 - Conference contribution
AN - SCOPUS:85124202271
T3 - BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings
BT - BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021
Y2 - 6 October 2021 through 9 October 2021
ER -