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apache / tvm-vta / 8bf06c3577569745128a3c1442fd80e728f82402
commit8bf06c3577569745128a3c1442fd80e728f82402[log] [tgz]
authortqchen <tqchen@octoml.ai>Wed Feb 12 15:38:39 2020 -0800
committerTianqi Chen <tqchen@users.noreply.github.com>Thu Feb 13 20:24:01 2020 -0800
tree0ab8683e335eaa6b7fe0e5adfeca98e5b4de7b06
parenta38b85121694e1eae5356e920e670ee39d3968eb [diff]
[REFACTOR][PY] Establish tvm.tir

- Move related files into the corresponding location as in C++
- Keep the top-level TVM API backward compatible to make minimum changes in topi
4 files changed
tree: 0ab8683e335eaa6b7fe0e5adfeca98e5b4de7b06
  1. apps/
  2. config/
  3. hardware/
  4. include/
  5. python/
  6. scripts/
  7. src/
  8. tests/
  9. tutorials/
  10. README.md
README.md

VTA: Open, Modular, Deep Learning Accelerator Stack

VTA (versatile tensor accelerator) is an open-source deep learning accelerator complemented with an end-to-end TVM-based compiler stack.

The key features of VTA include:

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

Learn more about VTA here.