OPTICS & OPTOELECTRONIC TECHNOLOGY, Volume. 20, Issue 5, 108(2022)
Design of Hardware Accelerator for Target Detection Based on Convolutional Neural Network
[1] [1] Smeulders, Arnold WM. Visual tracking: An experimental survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2014, 36(7): 1442-1468.
[2] [2] Zhao Y, Wang C, Gong L, et al. Deep learning accelerators[J]. High Technology Letter, 2019, 25(4): 9.
[3] [3] Chen Zhang, Peng Li, Guangyu Sun, et al. Optimizing fpga-based accelerator design for deep convolutional neural networks[C]. In Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, 2016, 161170. New York, United States.
[4] [4] Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]. IEEE Conference on Computer Vision & Pattern Recognition, 2017, 2053: 6517-6525.
[5] [5] Jiantao Qiu, Jie Wang, Song Yao, et al. Going deeper with embedded FPGA platform for convolutional neural network[C]. In Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, 2016, 2635. New York, United States.
[6] [6] Chao Wang, Lei Gong, Qi Yu, et al. DLAU: A scalable deep learning accelerator unit on FPGA[J]. IEEE Trans. on CAD of Integrated Circuits and Systems, 2017, 36(3): 513-517.
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CHENG Wen-shao, FAN Qiang, ZOU Er-bo. Design of Hardware Accelerator for Target Detection Based on Convolutional Neural Network[J]. OPTICS & OPTOELECTRONIC TECHNOLOGY, 2022, 20(5): 108
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Received: Mar. 24, 2022
Accepted: --
Published Online: Oct. 17, 2022
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