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apache / tvm-vta / 076cacbcb1c526161d3ba0d5f2a4e2473dfcac4e
commit076cacbcb1c526161d3ba0d5f2a4e2473dfcac4e[log] [tgz]
authorTianqi Chen <tqchen@users.noreply.github.com>Fri Feb 07 09:15:08 2020 -0800
committerGitHub <noreply@github.com>Fri Feb 07 09:15:08 2020 -0800
treef9f4b5786773b09ef9fee397ca9911559378b1b0
parentd60fb1c9a683738b877de02b7bb048fc943f76d4 [diff]
[REFACTOR][PY][API-Change] Polish tvm.runtime, tvm.runtime.module API update (#4837)

* [REFACTOR][PY-API] Polish tvm.runtime, tvm.runtime.module API update

This PR updates the tvm.runtime to use the new FFI style.

- Remove top-level tvm.module to avoid confusion between runtime.Module and IRModule
- API changes wrt to runtime.Module
  - tvm.module.load -> tvm.runtime.load_module
  - tvm.module.enabled -> tvm.runtime.enabled
  - tvm.module.system_lib -> tvm.runtime.system_lib
- Remove dep on api_internal from runtime.

* Update module.load in the latest API
15 files changed
tree: f9f4b5786773b09ef9fee397ca9911559378b1b0
  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.