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TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
[article]
2018
arXiv
pre-print
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
arXiv:1802.04799v3
fatcat:e6htzyqaqjhpnm3yyi6xl3mdoq