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apache / tvm-vta / 23657d26a869e71bed8373ecdc90340e47ebfbec
commit23657d26a869e71bed8373ecdc90340e47ebfbec[log] [tgz]
authorTianqi Chen <tqchen@users.noreply.github.com>Wed Feb 26 21:10:47 2020 -0800
committerGitHub <noreply@github.com>Wed Feb 26 21:10:47 2020 -0800
tree1b133b91b624463fa0c50520b545ab8838a37d07
parentbbe20bb347da90909c2ae9d345c2efb3481a5abd [diff]
[REFACTOR][PY][API-CHANGE] Remove legacy python files. (#4943)

* [REFACTOR][PY][API-CHANGE] Remove legacy python files.

Remove legacy python files.
Use the te namespace for most of the tensor expression primitives.

- tvm.create_schedule -> tvm.te.create_schedule
- tvm.placeholder -> tvm.te.placeholder
- tvm.compute -> tvm.te.compute

* Remove top-level exposures.
34 files changed
tree: 1b133b91b624463fa0c50520b545ab8838a37d07
  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.