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In-sensor neural network for high energy efficiency analog-to-information conversion

https://doi.org/10.1038/s41598-022-23100-4

Sadasivuni, Sudarsan; Bhanushali, Sumukh_Prashant; Banerjee, Imon; Sanyal, Arindam (October 2022, Scientific Reports)

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$$159\times $$ $159 \times$ with test-chips prototyped in 65 nm CMOS.
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A 43.6 TOPS/W AI Classifier with Sensor Fusion for Sepsis Onset Prediction

Sadasivuni, S.; Bhanushali, S.; Banerjee, I.; Sanyal, A. (January 2022, IEEE Biomedical Circuits and Systems Conference)

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Fully Integrated Analog Machine Learning Classifier Using Custom Activation Function for Low Resolution Image Classification

https://doi.org/10.1109/TCSI.2020.3047331

Chandrasekaran, Sanjeev Tannirkulam; Jayaraj, Akshay; Karnam, Vinay Elkoori; Banerjee, Imon; Sanyal, Arindam (January 2021, IEEE Transactions on Circuits and Systems I: Regular Papers)
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