17997

TVM: End-to-End Optimization Stack for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Paul G. Allen School of Computer Science & Engineering, University of Washington
arXiv:1802.04799 [cs.LG], (12 Feb 2018)

@article{chen2018endtoend,

   title={TVM: End-to-End Optimization Stack for Deep Learning},

   author={Chen, Tianqi and Moreau, Thierry and Jiang, Ziheng and Shen, Haichen and Yan, Eddie and Wang, Leyuan and Hu, Yuwei and Ceze, Luis and Guestrin, Carlos and Krishnamurthy, Arvind},

   year={2018},

   month={feb},

   archivePrefix={"arXiv"},

   primaryClass={cs.LG}

}

Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive the current popularity and utility of deep learning. However, these frameworks are optimized for a narrow range of server-class GPUs and deploying workloads to other platforms such as mobile phones, embedded devices, and specialized accelerators (e.g., FPGAs, ASICs) requires laborious manual effort. We propose TVM, an end-to-end optimization stack that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. We discuss the optimization challenges specific to deep learning that TVM solves: high-level operator fusion, low-level memory reuse across threads, mapping to arbitrary hardware primitives, and memory latency hiding. Experimental results demonstrate that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art libraries for low-power CPU and server-class GPUs. We also demonstrate TVM’s ability to target new hardware accelerator back-ends by targeting an FPGA-based generic deep learning accelerator. The compiler infrastructure is open sourced.
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