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apache / tvm-vta / 8558e4ee77a9aa104ec0397d3f9433e4a15337a3
commit8558e4ee77a9aa104ec0397d3f9433e4a15337a3[log] [tgz]
authorTianqi Chen <tqchen@users.noreply.github.com>Mon Feb 17 18:47:09 2020 -0800
committerGitHub <noreply@github.com>Mon Feb 17 18:47:09 2020 -0800
treeaf330064a93f6c1ea650d6e4cc3f6a0005958497
parent8bf06c3577569745128a3c1442fd80e728f82402 [diff]
[REFACTOR][PY] Establish tvm.te and tvm.driver (#4900)

- Move the related files to tvm.te
- Move build_module.py to tvm.driver
1 file changed
tree: af330064a93f6c1ea650d6e4cc3f6a0005958497
  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.