| Authors: | |
| Tianqi Chen | University of Washington |
| Thierry Moreau | University of Washington |
| Ziheng Jiang | Fudan University |
| Lianmin Zheng | Shanghai Jiao Tong University |
| Eddie Yan | University of Washington |
| Haichen Shen | University of Washington |
| Meghan Cowan | University of Washington |
| Leyuan Wang | UC Davis |
| Yuwei Hu | Cornell |
| Luis Ceze | University of Washington |
| Carlos Guestrin | University of Washington |
| Arvind Krishnamurthy | University of Washington |
One Sentence Summary:
There is an increasing need to bring machine learning to a wide diversity of hardware devices.the authors propose TVM, a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Abstract:
There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms -- such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) -- requires significant manual effort. We propose TVM, a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations. Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs. We also demonstrate TVM's ability to target new accelerator back-ends, such as the FPGA-based generic deep learning accelerator. The system is open sourced and in production use inside several major companies.