Key result
A customized CNN accelerator based on FPGA for pulse waveform classification achieved high accuracy with fewer parameters, costing only 0.714 W at 100 MHz.
Why the study?
Deploying convolutional neural network models for pulse waveform classification to low-power devices is challenging due to numerous parameters and intensive computation.
An FPGA-based CNN accelerator provides a feasible, low-power solution for real-time pulse waveform classification.
May enable portable real-time pulse analysis; leaves open clinical validation before practice adoption.
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-programmable gate array (FPGA), which can accurately and quickly infer the waveform category. By designing the structure of CNN, we significantly reduce its parameters on the premise of high accuracy. Then the CNN is realized on FPGA and optimized by a variety of memory access optimization methods. Experimental results show that our customized CNN has high accuracy and fewer parameters, and the accelerator costs only 0.714 W under a working frequency of 100 MHz, which proves that our proposed solution is feasible. Furthermore, the accelerator classifies the pulse waveform in real time, which could help doctors make the diagnosis.
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Chen et al. (2020) studied Pulse waveform classification. CNN accelerator based on a field-programmable gate array (FPGA) was evaluated on Accuracy and power consumption. A customized CNN accelerator based on FPGA for pulse waveform classification achieved high accuracy with fewer parameters, costing only 0.714 W at 100 MHz.
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