Design of handwritten digit recognition system based on FPGA

Siyu Zhu; Hu Huang; Zhihong Hu; Qian Tian

doi:10.1117/12.2600178

1 June 2021 Design of handwritten digit recognition system based on FPGA

Siyu Zhu, Hu Huang, Zhihong Hu, Qian Tian

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 1184812 (2021) https://doi.org/10.1117/12.2600178
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

Convolutional neural network(CNN) has achieved great success in various scenes of computer vision tasks. Nowadays, many computer vision applications need to be deployed on embedded devices. However, due to the huge amount of parameters and computation of CNN, many embedded devices are not competent for this requirement. Field programmable gate array(FPGA) has the characteristics of parallel computing and low power consumption, which makes it suitable for the deployment of the CNN model. In this paper, we use the high level synthesis(HLS) tool to design a convolution IP(Intellectual Property) core and a pooling IP core that can adapt to different kernel sizes and step sizes, and then deploy them on the ZYNQ7020 heterogeneous FPGA platform. Using these two basic modules, the inference of the CNN model can be well accelerated. Therefore, to verify our circuit architecture, this papre trains a CNN model for handwritten digit recognition. And after data quantification, this CNN model is finally deployed on this heterogeneous FPGA system. Under the 100MHz clock frequency, FPGA only needs 74ms to recognize a handwritten digital picture, and the accuracy rate is 98.89%.

Citation Download Citation

Siyu Zhu, Hu Huang, Zhihong Hu, and Qian Tian "Design of handwritten digit recognition system based on FPGA", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 1184812 (1 June 2021); https://doi.org/10.1117/12.2600178

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