Low-Power FPGA Implementation of Convolution Neural Network Accelerator for Pulse Waveform Classification

Low-Power FPGA Implementation of Convolution Neural Network Accelerator for Pulse Waveform Classification

In pulse waveform classification, the convolution neural network (CNN) shows excellent performance. However, due to its numerous parameters and intensive computation, it is challenging to deploy a CNN model to low-power devices. To solve this problem, we implement a CNN accelerator based on a field-...

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Bibliographic Details
Main Authors: Chuanglu Chen, Zhiqiang Li, Yitao Zhang, Shaolong Zhang, Jiena Hou, Haiying Zhang
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Algorithms
Subjects:
convolution neural network (CNN)
traditional Chinese medicine (TCM)
pulse waveform classification
field-programmable gate array (FPGA)
Online Access:https://www.mdpi.com/1999-4893/13/9/213

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https://www.mdpi.com/1999-4893/13/9/213

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