arrWNN: Arrhythmia-Detecting Weightless Neural Network FlexIC

This paper proposes a technique for incorporating machine learning into a wearable medical patch by combining two key technologies: weightless neural networks (WNNs), known for their efficiency and low hardware overhead, and Flexible Integrated Circuits (FlexICs) - ultra low-cost circuits on flexible substrates. We develop a special WNN model called “arrWNN” for detecting arrhythmia events from ECG signals that has an average prediction accuracy of 89% over the MIT BIH Arrhythmia datasets. We, then, design and implement the arrWNN model in hardware, and fabricate it using Pragmatic's FlexIC technology. The arrWNN FlexIC contains 5,706 NAND2-equivalent gates with a core area of 24 mm² consuming less than 10 mW at 3V. Our wafer-level test and measurement results show the full functionality of the fabricated arrWNN FlexICs validated against the simulation.