Xin Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xin Li has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Dehan Luo
School of Information Engineering, Guangdong University of Technology, Guangzhou, China
1 shared publication - Tengteng Wen
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
1 shared publication - Yu Cheng
School of Information Engineering, Guangdong University of Technology, Guangzhou, China
1 shared publication - Zhuofeng Mo
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
1 shared publication
Latest Publications
- Open Access
FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network
The integrated electronic nose (e-nose) design, which integrates sensor arrays and recognition algorithms, has been widely used in different fields. However, the current integrated e-nose system usually suffers from the problem of low accuracy with simple algorithm structure and slow speed with complex algorithm structure. In this article, we propose a method for implementing a deep neural network for odor identification in a small-scale Field-Programmable Gate Array (FPGA). First, a lightweight odor identification with depthwise separable convolutional neural network (OI-DSCNN) is proposed to reduce parameters and accelerate hardware implementation performance. Next, the OI-DSCNN is implemented in a Zynq-7020 SoC chip based on the quantization method, namely, the saturation-flooring KL divergence scheme (SF-KL). The OI-DSCNN was conducted on the Chinese herbal medicine dataset, and simulation experiments and hardware implementation validate its effectiveness. These findings shed light on quick and accurate odor identification in the FPGA.
Open AccessFPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network
The integrated electronic nose (e-nose) design, which integrates sensor arrays and recognition algorithms, has been widely used in different fields. However, the current integrated e-nose system usually suffers from the problem of low accuracy with simple algorithm structure and slow speed with complex algorithm structure. In this article, we propose a method for implementing a deep neural network for odor identification in a small-scale Field-Programmable Gate Array (FPGA). First, a lightweight odor identification with depthwise separable convolutional neural network (OI-DSCNN) is proposed to reduce parameters and accelerate hardware implementation performance. Next, the OI-DSCNN is implemented in a Zynq-7020 SoC chip based on the quantization method, namely, the saturation-flooring KL divergence scheme (SF-KL). The OI-DSCNN was conducted on the Chinese herbal medicine dataset, and simulation experiments and hardware implementation validate its effectiveness. These findings shed light on quick and accurate odor identification in the FPGA.