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apache / tvm-vta / 834ddf1b9a9c1aa18fb6a1b00fe6f59251a97480
commit834ddf1b9a9c1aa18fb6a1b00fe6f59251a97480[log] [tgz]
authorTianqi Chen <tqchen@users.noreply.github.com>Mon Nov 11 10:09:29 2019 -0800
committerGitHub <noreply@github.com>Mon Nov 11 10:09:29 2019 -0800
treefc2c68db7a6635e412f21db6202cfbbaa23891ab
parent2ad49d28fe42ec58415f5a260c70365b65b85944 [diff]
[RUNTIME][REFACTOR] Use object protocol to support runtime::Module (#4289)

Previously runtime::Module was supported using shared_ptr.
This PR refactors the codebase to use the Object protocol.

It will open doors to allow easier interpolation between
Object containers and module in the future.
1 file changed
tree: fc2c68db7a6635e412f21db6202cfbbaa23891ab
  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.