Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (7): 22-28.doi: 10.11896/jsjkx.230500220

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Study on Deep Learning Automatic Scheduling Optimization Based on Feature Importance

YANG Heng1,2, LIU Qinrang2, FAN Wang2, PEI Xue2, WEI Shuai2, WANG Xuan1,2   

  1. 1 College of Cyberspace Security,Zhengzhou University,Zhengzhou 450003,China
    2 Institute of Information Technology,Information Engineering University,Zhengzhou 450002,China
  • Received:2023-05-30 Revised:2023-10-13 Online:2024-07-15 Published:2024-07-10
  • About author:YANG Heng,born in 1998,postgra-duate.His main research interest is deep learning compiler.
    LIU Qinrang,born in 1975,Ph.D,professor,Ph.D supervisor.His main research interests include cyberspace security and chip design.
  • Supported by:
    Major Project of National Key R & D Program of China(2022YFB4401401) and Program of Songshan Laboratory(included in the management of Major Science and Technology Program of Henan Province)(221100211100-01).

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Abstract: With the rapid development of deep learning and hardware architectures,the diversity of models and hardware architectures make the deployment for deep learning models with high performance manually become increasingly challenging.So current AI compiler framework often adopts automatic scheduling.Since the existing optimization to TVM automatic scheduling has such issues as unbalanced data sets in cost model and overlong scheduling time,an automatic scheduling optimization strategy based on feature importance is designed in this paper.First,the feature importance is analyzed through the xgboost algorithm.Then a stra-tegy that reduce the data feature dimensions based on the importance coefficient and reassign the data labels is adopted to improve the precision of the cost model and optimize the efficiency of the automatic scheduling.Experiment results show that the proposed optimization method can reduce the automatic scheduling time of three kinds of deep learning models by 9.7%~17.2%,and reduce the inference time by up to 15%.

Key words: AI compiler, Automatic scheduling, xgboost, Feature importance, Deep learning

CLC Number: 

  • TP302
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