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apache / tvm-vta / 3a669d6216eefd718073ae9b91989913a1b25f03
commit3a669d6216eefd718073ae9b91989913a1b25f03[log] [tgz]
authorTianqi Chen <tqchen@users.noreply.github.com>Sun Jan 19 09:53:22 2020 -0800
committerGitHub <noreply@github.com>Sun Jan 19 09:53:22 2020 -0800
tree067f4b2bbe003328d721b55a83fb4da4820f4ca5
parent6bb74c0efd64c30f43353364a9bfb6923585d628 [diff]
[REFACTOR][CODEGEN] codegen->target, build_module->driver (#4742)

This PR moves the codegen related code into the target folder,
as they are target specific functionalities.

We also adopt the term "compiler driver" in common compiler infra
such as rust, GHC and clang.
As a result, build_module is moved into the driver folder.
1 file changed
tree: 067f4b2bbe003328d721b55a83fb4da4820f4ca5
  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.