An Energy Efficient ECG Ventricular Ectopic Beat Classifier Using Binarized CNN for Edge AI Devices
David Liang Tai Wong, Yongfu Li, Deepu John, Weng Khuen Ho, Chun-Huat Heng
Abstract
Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-efficient. In this paper, we design and implement an efficient binary convolutional neural network (bCNN) algorithm utilizing function-merging and block-reuse techniques to classify between Ventricular and non-Ventricular Ectopic Beat images. We deploy our model into a low-resource low-power field programmable gate array (FPGA) fabric. Our model achieves a classification accuracy of 97.3%, sensitivity of 91.3%, specificity of 98.1%, precision of 86.7%, and F1-score of 88.9%, along with dynamic power dissipation of only 10.5-μW.
Topics & Concepts
Convolutional neural networkComputer scienceField-programmable gate arrayArtificial neural networkEdge deviceArtificial intelligencePattern recognition (psychology)Classifier (UML)Wearable computerDissipationElectronic engineeringComputer hardwareEngineeringEmbedded systemOperating systemCloud computingThermodynamicsPhysicsECG Monitoring and AnalysisCardiac Arrhythmias and TreatmentsElectrostatic Discharge in Electronics