In the paper,based on Xilinx Vitis AI,the semantic segmentation network U-Net is implemented with network fixed point,deep learning processing unit DPU customization,software and hardware collaborative optimization and other acceleration methods.Finally,the design of the semantic segmentation accelerator is implemented on the Xilinx ZCU102 heterogeneous platform.The hardware resource consumption is reduced with low precision loss,and the software and hardware system development of the entire U-Net network is completed.The experimental results show that the processing frame rate of the entire U-Net network hardware accelerator can reach 42 fps,which shows the effectiveness of the neural network acceleration scheme.
Key words
FPGA /
deep learning processing unit /
semantic segmentation /
Vitis AI /
convolutional neural networks
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] Yuan X,Shi J,Gu L.A review of deep learning methods for semantic segmentation of remote sensing imagery[J].Expert Systems with Applications,2021(169):114417.
[2] 徐辉,祝玉华,甄彤,等.深度神经网络图像语义分割方法综述[J].计算机科学与探索,2021,15(1):47.
[3] Asgari Taghanaki S,Abhishek K,Cohen J P,et al.Deep semantic segmentation of natural and medical images:a review[J].Artificial Intelligence Review,2021,54(1):137-178.
[4] 梅亚军,王唯佳,彭析竹.基于FPGA的U-Net网络硬件加速系统的实现[J].电子与封装,2020,20(6):40-45.
[5] Ronneberger O,Fischer P,Brox T.U-net:Convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention.Springer,Cham,2015:234-241.
[6] 王赛男,郑雄风.基于边缘计算的图像语义分割应用与研究[J].计算机科学,2020,47(11A):276-280.
[7] Long J,Shelhamer E,Darrell T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition,2015:3431-3440.
[8] Badrinarayanan V,Kendall A,Cipolla R.Segnet:A deep convolutional encoder-decoder architecture for image segmentation[J].IEEE transactions on pattern analysis and machine intelligence,2017,39(12):2481-2495.
[9] Wu D,Liao M,Zhang W,et al.Yolop:You only look once for panoptic driving perception[J].arXiv preprint arXiv:2108.11250,2021.
[10] Chowdhuri S,Pankaj T,Zipser K.Multinet:Multi-modal multi-task learning for autonomous driving[C]//2019 IEEE Winter Conference on Applications of Computer Vision (WACV).IEEE,2019:1496-1504.
[11] Li B,Zhao J,Fu H.DLT-Net:deep learning transmittance network for single image haze removal[J].Signal,Image and Video Processing,2020,14(6):1245-1253.
[12] 刘腾达,朱君文,张一闻.FPGA 加速深度学习综述[J].计算机科学与探索,2021,15(11):2093-2104.
[13] Hu Y,Liu Y,Liu Z.A Survey on Convolutional Neural Network Accelerators:GPU,FPGA and ASIC[C]//2022 14th International Conference on Computer Research and Development (ICCRD).IEEE,2022:100-107.
PDF(1244 KB)
