19720

The Deep Learning Compiler: A Comprehensive Survey

Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Depei Qian
School of Computer Science and Engineering, Beihang University, China
arXiv:2002.03794 [cs.DC], (6 Feb 2020)

@misc{li2020deep,

   title={The Deep Learning Compiler: A Comprehensive Survey},

   author={Mingzhen Li and Yi Liu and Xiaoyan Liu and Qingxiao Sun and Xin You and Hailong Yang and Zhongzhi Luan and Depei Qian},

   year={2020},

   eprint={2002.03794},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

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The difficulty of deploying various deep learning (DL) models on diverse DL hardwares has boosted the research and development of DL compilers in the community. Several DL compilers have been proposed from both industry and academia such as Tensorflow XLA and TVM. Similarly, the DL compilers take the DL models described in different DL frameworks as input, and then generate optimized codes for diverse DL hardwares as output. However, none of the existing survey has analyzed the unique design of the DL compilers comprehensively. In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations. Specifically, we provide a comprehensive comparison among existing DL compilers from various aspects. In addition, we present detailed analysis of the multi-level IR design and compiler optimization techniques. Finally, several insights are highlighted as the potential research directions of DL compiler. This is the first survey paper focusing on the unique design of DL compiler, which we hope can pave the road for future research towards the DL compiler.
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