“Introduction to the TVM Open Source Deep Learning Compiler Stack,” a Presentation from the University of Washington

Luis Ceze, a Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, co-founder and CEO of OctoML, and Venture Partner at Madrona Venture Group, presents the “Introduction to the TVM Open Source Deep Learning Compiler Stack” tutorial at the September 2020 Embedded Vision Summit.

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 — requires significant manual effort.

In this talk, Ceze presents his work on the TVM stack, which exposes graph- and operator-level optimizations to provide performance portability for 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 optimizations.

See here for a PDF of the slides.

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