[1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84-90.
[4] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA. 2016: 770-778.
[5] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [C]// 3rd International Conference on Learning Representations (ICLR). San Diego, CA, USA. 2015: 659-673.
[6] IANDOLA F N, HAN S, MOSKEWICZ M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and<0.5 MB model size [EB/OL]. https://arxiv.org/abs/1409.1556, 2016.
[8] GSCHWEND D. Zynqnet: an FPGA-accelerated embedded convolutional neural network [EB/OL]. https://arxiv.org/abs/2005.06892v1, 2020.
[9] NúEZ-PRIETO R, GOMEZ P C, LIU L, et al. A real-time gesture recognition system with FPGA accelerated ZynqNet classification [C]// 2019 IEEE Nordic Circuits and Systems Conference (NORCAS) / NORCHIP and International Symposium of System-on-Chip (SoC). Helsinki, Finland. 2019: 1-6.
[11] ABD EL-MAKSOUD A J, GAMAL A, HESHAM A, et al. Hardware-accelerated ZYNQ-NET convolutional neural networks on Virtex-7 FPGA [C]// 2021 International Conference on Microelectronics (ICM). New Cairo City, Egypt. 2021: 70-73.