Design of Hybrid Multimode Convolutional Neural NetworkAccelerator for Electrocardiogram Detection br

With the increasing scarcity of medical resources and the aging of the population, cardiovasculardisease has posed a great threat to human health. Portable devices with ElectroCardioGram (ECG) detectioncan effectively reduce the threat of cardiovascular disease to patients. In this paper, a hybrid multi-modeConvolutional Neural Network(CNN) accelerator is designed for monitoring the patient's ECG. Firstly, a one-Dimensional Convolutional Neural Network(1D-CNN) model is introduced for ECG classification, then anefficient accelerator is designed for this model, which adopts a multi-parallel expansion strategy and multi-datastream operation mode to complete the acceleration and optimization of convolution loops. The proposedoperation mode can highly reuse data in time and space, and improve the utilization of hardware resources,thereby improving the hardware efficiency of the hardware accelerator. Finally, the prototype verification is completed based on the Xilinx ZC706 hardware platform. The results show 2247 LUTs and 80 DSPs are consumed. At200 MHz operating frequency, the overall performance can reach 28.1 GOPS, and the hardware efficiencyreaches 12.82 GOPS/kLUT.