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apache / tvm-vta / a38b85121694e1eae5356e920e670ee39d3968eb
commita38b85121694e1eae5356e920e670ee39d3968eb[log] [tgz]
authortqchen <tqchen@octoml.ai>Wed Feb 12 13:39:45 2020 -0800
committerTianqi Chen <tqchen@users.noreply.github.com>Wed Feb 12 16:36:23 2020 -0800
tree99d24774f35cb35cff1e01ef63bc856317d4e6ba
parent36e08acbc3c1c7fc5fe6706720920216e658cf3b [diff]
[REFACTOR][PY][API-CHANGE] Establish tvm.target

Move the related target modules into tvm.target.

API change:
- tvm.target.current_target -> tvm.target.Target.current
- tvm.datatype -> tvm.target.datatype
7 files changed
tree: 99d24774f35cb35cff1e01ef63bc856317d4e6ba
  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.