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apache / tvm-vta / 9b178fdb387bffc708f2448a82e85b4737239aed
commit9b178fdb387bffc708f2448a82e85b4737239aed[log] [tgz]
authorLuis Vega <vegaluisjose@users.noreply.github.com>Wed Sep 02 11:13:11 2020 -0700
committerGitHub <noreply@github.com>Wed Sep 02 11:13:11 2020 -0700
treeab6fd6f235a4d5237b29b449c396beeb9d619ddd
parentf1c3385e2042e876c719d1b30dd90ed533c51ed7 [diff]
fixes for vivado 2020.1 (#10)
1 file changed
tree: ab6fd6f235a4d5237b29b449c396beeb9d619ddd
  1. apps/
  2. config/
  3. hardware/
  4. include/
  5. src/
  6. tests/
  7. .asf.yaml
  8. .gitignore
  9. Jenkinsfile
  10. LICENSE
  11. NOTICE
  12. README.md
README.md

VTA Hardware Design Stack

Build Status

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.