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127 / 2021-04-10 16:43:43
A low-cost Hardware Architecture of Convolutional Neural Network for ECG Classification
electrocardiogram (ECG) classification,convolutional neural network (CNN),neural network hardware,low-cost hardware architecture
IOT and AI Techniques > Artificial Intelligence
终稿
Lai Wei / Huazhong University of Science and Technology
Dongsheng Liu / Huazhong University of Science and Technology
Jiahao Lu / Huazhong University of Science and Technology
Lingsong Zhu / Huazhong University of Science and Technology
Xuan Cheng / Huazhong University of Science and Technology
Convolutional neural network (CNN) is one of the most important structure of the machine learning and have been widely used in medical diagnosis due to its effectiveness. The main aim of this study is to build a 1-D CNN model for electrocardiogram (ECG) classification and design a low-cost hardware architecture of this neural network, and this paper propose a novel architecture using efficient convolution PE array to reduce resource utilization and improve computing efficiency. The proposed CNN acceleration scheme and architecture are demonstrated by implementing end-to-end the proposed 1-D CNN for inference on FPGA Zynq ZC706, the overall throughput achieves 26.6GOPS consuming only 2.51k LUTs and 0.79W at 200MHz, and the hardware efficiency is 10.59 GOP/s/kLUT.
重要日期

  • 会议日期

    07月10日

    2021

    至

    07月12日

    2021

  • 05月10日 2021

    初稿截稿日期

  • 07月06日 2021

    注册截止日期

主办单位
长沙理工大学
协办单位
IEEE Electron Devices Society
IEEE
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