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apache / tvm-vta / d5d1f749052afb1aff27bdae80d7bcd72e62a73e
commitd5d1f749052afb1aff27bdae80d7bcd72e62a73e[log] [tgz]
authortqchen <tianqi.tchen@gmail.com>Fri Jul 06 19:22:50 2018 -0700
committerTianqi Chen <tqchen@users.noreply.github.com>Wed Jul 11 21:54:39 2018 -0700
treee94889baf10cc9ea604f91f046279ec592da66d7
parentea9e519d020ea302e87de340c2731d37bf618645 [diff]
[BUILD][DOCS] Migrate VTA CI, test, build, docs
63 files changed
tree: e94889baf10cc9ea604f91f046279ec592da66d7
  1. config/
  2. hardware/
  3. include/
  4. python/
  5. src/
  6. tests/
  7. tutorials/
  8. README.md
README.md

VTA: Open, Modular, Deep Learning Accelerator Stack

VTA(versatile tensor accelerator) is an open-source deep learning accelerator stack. It is not just an open-source hardware, but is an end to end solution that includes the entire software stack on top of VTA open-source hardware.

The key features include:

  • Generic, modular open-source hardware
    • Streamlined workflow to deploy to FPGAs.
    • Simulator support to protoype compilation passes on regular workstations.
  • Driver and JIT runtime for both simulated and FPGA hardware backend.
  • End to end TVM stack integration
    • Direct optimization and deploy models from deep learning frameworks via TVM stack.
    • Customized and extendible TVM compiler backend.
    • Flexible RPC support to ease the deployment, and program FPGAs with Python

VTA is part of our effort on TVM Stack.