• Journal of Semiconductors
  • Vol. 41, Issue 2, 022406 (2020)
Weixiong Jiang1、2、3, Heng Yu4, Jiale Zhang1、2、3, Jiaxuan Wu1、2、3, Shaobo Luo5, and Yajun Ha1、2、3
Author Affiliations
  • 1School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
  • 2Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4University of Nottingham Ningbo China, Ningbo 315100, China
  • 5Universite Paris-Est, Paris 93162, France
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    DOI: 10.1088/1674-4926/41/2/022406 Cite this Article
    Weixiong Jiang, Heng Yu, Jiale Zhang, Jiaxuan Wu, Shaobo Luo, Yajun Ha. Optimizing energy efficiency of CNN-based object detection with dynamic voltage and frequency scaling[J]. Journal of Semiconductors, 2020, 41(2): 022406 Copy Citation Text show less
    System architecture of CNN accelerator.
    Fig. 1. System architecture of CNN accelerator.
    MMCM.
    Fig. 2. MMCM.
    Dynamically reconfiguring MMCM using AXI4-Lite interface.
    Fig. 3. Dynamically reconfiguring MMCM using AXI4-Lite interface.
    Pseudocode of the driver for dynamic frequency scaling.
    Fig. 4. Pseudocode of the driver for dynamic frequency scaling.
    Power Management Framework on FPGA.
    Fig. 5. Power Management Framework on FPGA.
    Power monitoring system topology on ZCU104.
    Fig. 6. Power monitoring system topology on ZCU104.
    Pseudocode of the driver for dynamic voltage scaling.
    Fig. 7. Pseudocode of the driver for dynamic voltage scaling.
    (a) Timing diagram illustrating the DVFS policy with enough time to perform DVS. (b) Timing diagram illustrating the DVFS policy without enough time to perform DVS.
    Fig. 8. (a) Timing diagram illustrating the DVFS policy with enough time to perform DVS. (b) Timing diagram illustrating the DVFS policy without enough time to perform DVS.
    Total time change with with frequency respectively.
    Fig. 9. Total time change with with frequency respectively.
    (Color online) PL side power change with voltage and frequency respectively.
    Fig. 10. (Color online) PL side power change with voltage and frequency respectively.
    (Color online) Total energy changes with voltage and frequency respectively.
    Fig. 11. (Color online) Total energy changes with voltage and frequency respectively.
    (Color online) IoU changes with frequency at 840 mV.
    Fig. 12. (Color online) IoU changes with frequency at 840 mV.
    (Color online) Average of IoU changes with voltage and frequency respectively.
    Fig. 13. (Color online) Average of IoU changes with voltage and frequency respectively.
    (Color online) UEE changes with voltage and frequency respectively.
    Fig. 14. (Color online) UEE changes with voltage and frequency respectively.
    (Color online) Idle power changes with voltage and frequency respectively.
    Fig. 15. (Color online) Idle power changes with voltage and frequency respectively.
    (Color online) Average power changes with voltage and frequency respectively.
    Fig. 16. (Color online) Average power changes with voltage and frequency respectively.
    ResourceLUTLUTRAMFFBRAMDSP
    SkyNet total54639198465196209333
    Accelerator4993492157101209333
    AXI bus46911062803000
    System reset1416500
    SkyNet DVFS total56902208466781209333
    Accelerator4991092156095209333
    AXI bus57991161912700
    DFS module11640149200
    System reset2926700
    Total2304001017604608003121728
    Table 1. Resource utilization of the system.
    ParameterPerformanceEnergy efficiencyIoUUEE
    SkyNet[25]23.93 FPS2.02 FPJ71.91%3.49
    Candidate 137.04 FPS, 1.54 ×2.79 FPJ, 1.38 ×71.91%7.22, 2.06 ×
    Candidate 237.42 FPS, 1.56 ×2.97 FPJ, 1.47 ×71.80%7.71, 2.21 ×
    Table 2. Comparison with other work.
    Weixiong Jiang, Heng Yu, Jiale Zhang, Jiaxuan Wu, Shaobo Luo, Yajun Ha. Optimizing energy efficiency of CNN-based object detection with dynamic voltage and frequency scaling[J]. Journal of Semiconductors, 2020, 41(2): 022406
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