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apache / tvm-vta / 36e08acbc3c1c7fc5fe6706720920216e658cf3b
commit36e08acbc3c1c7fc5fe6706720920216e658cf3b[log] [tgz]
authorTianqi Chen <tqchen@users.noreply.github.com>Tue Feb 11 20:01:36 2020 -0800
committerGitHub <noreply@github.com>Tue Feb 11 20:01:36 2020 -0800
tree2fe0b2602077ab941a74039595b4d14474a3b315
parent651bdc4533edd6171910b065efbbfacb2b5af29a [diff]
[REFACTOR][PY][API-CHANGE] establish tvm.ir, migrate corresponding files (#4862)

* [REFACTOR][PY][API-CHANGE] establish tvm.ir, migrate corresponding relay files.

This PR establishes tvm.ir and migrates the corresponding relay
files into the new folder.

API Change:
- relay.Module -> tvm.IRModule

* Update with ADT

* Migrate transform

* address comments

* Migrate module

* Migrate json_compact

* Migrate attrs

* Move LoweredFunc to stmt temporarily

* temp migrate container

* Finish migrate container
3 files changed
tree: 2fe0b2602077ab941a74039595b4d14474a3b315
  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.