| [1] |
TURNER V,GANTZ J F,REINSEL D,et al.The digital universe of opportunities:rich data and the increasing value of the Internet of things[EB/OL].[2019-09-01].https://www.docin.com/p-1440446108.html.
|
| [2] |
SHI Weisong,SUN Hui,CAO Jie,et al.Edge computing-an emerging computing model for the Internet of everything era[J].Journal of Computer Research and Development,2017,54(5):907-924.
|
| [3] |
SHI Weisong,CAO Jie,ZHANG Quan,et al.Edge computing:vision and challenges[J].IEEE Internet of Things Journal,2016,3(5):637-646.
|
| [4] |
ZHANG Xingzhong,WANG Yifan,SHI Weisong.PCAMP:performance comparison of machine learning packages on the edges[C]//Proceedings of IEEE International Workshop on Hot Topics in Edge Computing.Washington D.C.,USA:IEEE Press,2018:1-10.
|
| [5] |
RUNGSUPWEEKOON K,VISOOTTIVISETH V,TAKANO R.Evaluating the power efficiency of deep learning inference on embedded GPU systems[C]//Proceedings of the 2nd IEEE International Conference on Informa-tion Technology.Washington D.C.,USA:IEEE Press,2017:1-5.
|
| [6] |
ZHENG Junfeng,LI Jiangyun,LIU Yan,et al.Real-time semantic segmentation network for edge deployment[C]//Proceedings of CISC'19.Berlin,Germany:Springer,2019:123-134.
|
| [7] |
HIMAVATHI S,ANITHA D,MUTHURAMALINGAM A.Feedforward neural network implementation in FPGA using layer multiplexing for effective resource utilization[J].IEEE Transactions on Neural Networks,2007,18(3):880-888.
|
| [8] |
HIROKI N,SHIMODA M,SATO S.A demonstration of FPGA-based you only look once version2[C]//Proceedings of the 28th International Conference on Field Programmable Logic and Applications.Washington D.C.,USA:IEEE Press,2018:434-445.
|
| [9] |
GUO Kaiyuan,ZENG Shulin,YU Jingcheng,et al.A survey of fpga-based neural network accelerator[EB/OL].[2019-09-01].https://arxiv.org/abs/1712.08934.
|
| [10] |
WILLIAMS S,WATERMAN A,PATTERSON D.Roofline:an insightful visual performance model for floating-point programs and multicore architectures[EB/OL].[2019-09-01].http://www.doc88.com/p-8969745080678.html.
|
| [11] |
LAUDON J.Performance/watt:the new server focus[J].ACM SIGARCH Computer Architecture News,2005,33(4):5-13.
|
| [12] |
SANDLER M,HOWARD A,ZHU M,et al.Mobilenetv2:inverted residuals and linear bottlenecks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press, 2018:4510-4520.
|
| [13] |
SZEGEDY C,IOFFE S,VANHOUCKE V,et al.Inception-v4,inception-resnet and the impact of residual connections on learning[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence.Washington D.C.,USA:IEEE Press,2017:158-167.
|
| [14] |
HOWARD A G,ZHU M,CHEN B,et al.MobileNets:efficient convolutional neural networks for mobile vision applications[EB/OL].[2019-09-01].https://arxiv.org/pdf/1704.04861.pdf.
|
| [15] |
SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:1-9.
|
| [16] |
JOUPPI N P,YOUNG C,PATIL N,et al.In-datacenter performance analysis of a tensor processing unit[C]//Proceedings of the 44th ACM/IEEE Annual International Symposium on Computer Architecture.Washington D.C.,USA:IEEE Press,2017:1-12.
|
| [17] |
JOUPPI N,YOUNG C,PATIL N,et al.Motivation for and evaluation of the first tensor processing unit[J].IEEE Micro,2018,38(3):10-19.
|
| [18] |
ASANOVIC K,ARBIB M A.Programmable neuro computing in the handbook of brain theory and neural networks[M].[S.1]:MIT Press,2002:223-235.
|
| [19] |
KING A,BHANGDARKAR S M,HOPKINSON B M.A comparison of deep learning methods for semantic segmentation of coral reef survey images[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:1394-1402.
|
| [20] |
REAGEN B,WHATMOUGH P,ADOLF R,et al.Minerva:enabling low-power highly-accurate deep neural network accelerators[C]//Proceedings of International Conference on Computer Architecture.Washington D.C.,USA:IEEE Press,2016:336-348.
|
| [21] |
HONG S,KIM H.An integrated GPU power and performance model[J].ACM SIGARCH Computer Architecture News,2010,38(3):280-289.
|
| [22] |
RODRIGUES R,ANNAMALAI A,KOREN I,et al.Performance per watt benefits of dynamic core morphing in asymmetric multicores[C]//Proceedings of 2011 International Conference on Parallel Architectures and Compilation Techniques.Washington D.C.,USA:IEEE Press,2011:121-130.
|
| [23] |
OFENBECK G,STEINMANN R,CAPARROS V,et al.Applying the roofline model[C]//Proceedings of 2014 IEEE International Symposium on Performance Analysis of Systems and Software.Washington D.C.,USA:IEEE Press,2014:76-85.
|
| [24] |
ASANOVIC K,JOHNSON J,BECK J.et al.Parallel architectures for artificial networks:paradigms and implementations[EB/OL].[2019-09-01].https://people.eecs.berkeley.edu/-krste/papers/annbook.pdf.
|
| [25] |
CHEN Y J,SZE E V.Eyeriss:a spatial architecture for energy-efficient dataflow for convolutional neural networks[C]//Proceedings of International Conference on Computer Architecture.Washington D.C.,USA:IEEE Press,2016:1564-1578.
|
| [26] |
BARROSO L A,HOLZLE U.The case for energy-proportional computing[J].IEEE Computer,2007,40(12):33-37.
|
| [27] |
HONG S,KIM H.An integrated GPU power and performance model[J].ACM SIGARCH Computer Architecture News,2010,38(3):280-289.
|
| [28] |
RODRIGUES R,ANNAMALAI A,KOREN I,et al.Performance per watt benefits of dynamic core morphing in asymmetric multicores[C]//Proceedings of 2011 International Conference on Parallel Architectures and Compilation Techniques.Washington D.C.,USA:IEEE Press,2011:121-130.
|
| [29] |
CHOI J W,BEDARD D,FOELER R,et al.A roofline model of energy[C]//Proceedings of the 27th IEEE International Symposium on Parallel and Distributed Processing.Washington D.C.,USA:IEEE Press,2013:661-672.
|