Abstract Details
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
Final Paper
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.
Important Date
-
Conference Date
Jul 10
2021
toJul 12
2021
-
May 10 2021
Draft paper submission deadline
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Jul 06 2021
Registration deadline
Sponsored By
Changsha University of Science & Technology
Supported By
IEEE Electron Devices Society
IEEE
IEEE
Contact Information
- Dr. WanghuiZou
- zo******@csust.edu.cn
- 135********
Previous Conferences
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2018-05-07 Taiwan, China
2018 7th International Symposium on Next Generation Electronics -
2017-05-23 Taiwan, China Keelung
2017 6th International Symposium on Next Generation Electronics
