Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (2): 299-309.doi: 10.11896/jsjkx.240900101

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Adaptive Operator Parallel Partitioning Method for Heterogeneous Embedded Chips in AIoT

LIN Zheng1, LIU Sicong1, GUO Bin1, DING Yasan1, YU Zhiwen1,2   

  1. 1 College of Computer Science,Northwestern Polytechnical University,Xi'an 710072,China
    2 Harbin Engineering University,Harbin 150001,China
  • Received:2024-09-16 Revised:2024-11-02 Online:2025-02-15 Published:2025-02-17
  • About author:LIN Zheng,born in 2002,postgraduate.His main research interests include ubiquitous computing,and mobile crowd sensing.
    GUO Bin,born in 1980,Ph.D,Ph.D supervisor,is a member of CCF(No.E200019107S).His main research interests include ubiquitous computing,and mobile crowd sensing.
  • Supported by:
    National Science Fund for Distinguished Young Scholars of China(62025205) and National Natural Science Foundation of China(62032020, 62302017).

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Abstract: With the continuous improvement of people's quality of life and the rapid development of technology,mobile devices such as smartphones have achieved widespread popularity globally.Against this backdrop,the deployment and application of deep neural networks on mobile devices have become a research hotspot.Deep neural networks not only drive significant progress in the field of mobile applications,but also pose higher requirements for energy efficiency management of battery-powered mobile devices.Meanwhile,the rise of heterogeneous processors in today's mobile devices brings new challenges to energy efficiency optimization.The allocation of computing tasks among different processors to achieve parallel processing and acceleration of deep neural networks does not necessarily optimize energy consumption and may even increase it.To address this issue,this paper proposes an energy-efficient adaptive parallel computing scheduling system for deep neural networks.This system comprises a runtime energy consumption analyzer and an online operator partitioning executor,which can dynamically adjust operator allocation based on dynamic device conditions,ensuring optimized energy efficiency for computing on heterogeneous processors of mobile devices while maintaining high responsiveness.Experimental results demonstrate that compared to baseline methods,the system designed in this paper reduces average energy consumption and latency by 5.19% and 9.0% respectively,and the maximum energy consumption and latency are reduced by 18.35% and 21.6% on mobile device deep neural networks.

Key words: Deep neural networks, Mobile device, Energy efficiency optimization, Heterogeneous processors, Energy consumption prediction

CLC Number: 

  • TP391
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