Electronics Optics & Control, Volume. 28, Issue 2, 7(2021)

A Low-Cost Image Classification System Using Sparse Convolution Neural Network

FENG Siyi... ZHAO Tianfeng, CHEN Cheng, LI Yan and XU Hongmei* |Show fewer author(s)
Author Affiliations
  • [in Chinese]
  • show less
    References(14)

    [1] [1] LANE N D,BHATTACHARYA S,GEORGIEV P,et al.An early resource characterization of deep learning on wearables,smartphones and internet-of-things devices[C]//Proceedings of the International Workshop on Internet of Things Towards Applications,ACM,2015: 7-12.

    [2] [2] ALIPPI C,DISABATO S,ROVERI M.Moving convolutional neural networks to embedded systems: the AlexNet and VGG-16 case[C]//Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks,2018: 212-223.

    [3] [3] HOCHSTETLER J,PADIDELA R,CHEN Q,et al.Embedded deep learning for vehicular edge computing[C]//IEEE/ACM Symposium on Edge Computing (SEC),2018: 341-343.

    [4] [4] HOWARD A G,ZHU M L,CHEN B,et al.Mobilenets: efficient convolutional neural networks for mobile vision applications[EB/OL].(2017-04-17)[2020-01-04].https: //arxiv.org/pdf/1704.04861.pdf.

    [5] [5] REDDY B,KIM Y H,YUN S,et al.Real-time driver drowsiness detection for embedded system using model compression of deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops,2017: 121-128.

    [6] [6] HU Q H,WANG P S,CHENG J.From hashing to CNNs: training binaryweight networks via hashing[EB/OL].(2018-02-08)[2020-01-04].https: //arxiv.org/pdf/1802.02733.pdf.

    [7] [7] LI G,LI F,ZHAO T,et al.Block convolution: towards memory-efficient inference of large-scale CNNs on FPGA[C]//Design,Automation & Test in Europe Conference & Exhibition (DATE),IEEE,2018: 1163-1166.

    [9] [9] JADERBERG M,VEDALDI A,ZISSERMAN A.Speeding up convolutional neural networks with low rank expansions[C]//Proceedings of the British Machine Vision Conference,2014: 1-12.

    [10] [10] LIU B,WANG M,FOROOSH H,et al.Sparse convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015: 806-814.

    [11] [11] HAN S,POOL J,TRAN J,et al.Learning both weights and connections for efficient neural network[C]//Advances in Neural Information Processing Systems,2015: 1135-1143.

    [12] [12] WEN W,WU C,WANG Y,et al.Learning structured sparsity in deep neural networks[C]//Advances in Neural Information Processing Systems,2016: 2074-2082.

    [13] [13] YUAN M,LIN Y.Model selection and estimation in regression with grouped variables[J].Journal of the Royal Statistical Society: Series B(Statistical Methodology),2006,68(1): 49-67.

    [14] [14] KRIZHEVSKY A,HINTON G.Learning multiple layers of features from tiny images[R].Toronto: University of Toronto,2009.

    [15] [15] SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015: 1-9.

    Tools

    Get Citation

    Copy Citation Text

    FENG Siyi, ZHAO Tianfeng, CHEN Cheng, LI Yan, XU Hongmei. A Low-Cost Image Classification System Using Sparse Convolution Neural Network[J]. Electronics Optics & Control, 2021, 28(2): 7

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Jan. 4, 2020

    Accepted: --

    Published Online: Aug. 27, 2021

    The Author Email: Hongmei XU (holly_89301@cust.edu.cn)

    DOI:10.3969/j.issn.1671-637x.2021.02.002

    Topics