Microelectronics, Volume. 53, Issue 5, 841(2023)

Design of a Buffer Optimization Architecture for ZynqNet Hardware Accelerator

CHEN Zhuo1, CHEN Yiduo1, TIAN Chunsheng2, QIU Peiyi3, and DI Zhixiong1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    References(7)

    [1] [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] [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] [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] [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] [8] GSCHWEND D. Zynqnet: an FPGA-accelerated embedded convolutional neural network [EB/OL]. https://arxiv.org/abs/2005.06892v1, 2020.

    [9] [9] NúEZ-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] [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.

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    CHEN Zhuo, CHEN Yiduo, TIAN Chunsheng, QIU Peiyi, DI Zhixiong. Design of a Buffer Optimization Architecture for ZynqNet Hardware Accelerator[J]. Microelectronics, 2023, 53(5): 841

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    Paper Information

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    Received: Mar. 4, 2023

    Accepted: --

    Published Online: Jan. 3, 2024

    The Author Email:

    DOI:10.13911/j.cnki.1004-3365.230098

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