Design of Picture Classification System Based on Embedded PYNQ Architecture

Author:

Wang Shengkai1,Li Jun1,Meng Yuan1

Affiliation:

1. Information College, Yunnan University, Kunming China

Publisher

ACM

Reference10 articles.

1. Fraser , N. J. , Umuroglu , Y. , Gambardella , G. , Blott , M. , Leong , P. , & Jahre , M. , ( 2017 ). Scaling Binarized Neural Networks on Reconfigurable Logic. PARMA-DITAM at HiPEAC'17 . Fraser, N. J., Umuroglu, Y., Gambardella, G., Blott, M., Leong, P., & Jahre, M., et al. (2017). Scaling Binarized Neural Networks on Reconfigurable Logic. PARMA-DITAM at HiPEAC'17.

2. Qin , H. , Gong , R. , Liu , X. , Bai , X. , Song , J. , & Sebe , N.. ( 2020 ). Binary neural networks: a survey. Pattern Recognition, 107281 . Qin, H., Gong, R., Liu, X., Bai, X., Song, J., & Sebe, N.. (2020). Binary neural networks: a survey. Pattern Recognition, 107281.

3. Wu , J. , Cong , L. , Wang , Y. , Hu , Q. , & Jian , C.. ( 2016 ). Quantized Convolutional Neural Networks for Mobile Devices. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. Wu, J., Cong, L., Wang, Y., Hu, Q., & Jian, C.. (2016). Quantized Convolutional Neural Networks for Mobile Devices. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.

4. Li , J. , Cheng , L. , Wei , C. , Zhou , X. , & Wang , L.. ( 2017 ). Accelerating low bit-width convolutional neural networks with embedded FPGA . 2017 27th International Conference on Field Programmable Logic and Applications (FPL). IEEE. Li, J., Cheng, L., Wei, C., Zhou, X., & Wang, L.. (2017). Accelerating low bit-width convolutional neural networks with embedded FPGA. 2017 27th International Conference on Field Programmable Logic and Applications (FPL). IEEE.

5. Courbariaux , M. , Hubara , I. , Soudry , D. , El-Yaniv , R. , & Bengio , Y.. ( 2016 ). Binarized neural networks: training deep neural networks with weights and activations constrained to +1 or -1 . Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y.. (2016). Binarized neural networks: training deep neural networks with weights and activations constrained to +1 or -1.

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