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29 November 2021 Case study of convolutional neural network implemented on FPGA
Yicheng Zhong, Yong Zhang
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Proceedings Volume 12080, 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021); 120800P (2021) https://doi.org/10.1117/12.2618253
Event: 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021), 2021, Nanchang, China
Abstract
Convolutional neural network (CNN) has become a key research area in artificial intelligent technology. A case study of garbage identification and classification using convolutional neural network is presented in this paper. The CNN model with five convolutional layers is used to extract feature maps from garbage pictures, and classifies six types of domestic garbage. ZYNQ-7020 is employed to deploy the CNN model in order to take advantage of high efficiency, low power consumption and flexibility of FPGA platform. Additionally, the classification results are obtained at the FPGA terminal by running Python code through PYNQ. The study demonstrates an efficient approach to practically deploy CNN model and implement image classification on FPGA platform.
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Yicheng Zhong and Yong Zhang "Case study of convolutional neural network implemented on FPGA", Proc. SPIE 12080, 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021), 120800P (29 November 2021); https://doi.org/10.1117/12.2618253
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KEYWORDS
Field programmable gate arrays
Data modeling
Convolutional neural networks
Digital signal processing
Neural networks
Performance modeling
Library classification systems
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