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apache / tvm-vta / da299fcd85fb2129ed89bbe583263d84840993c9
commitda299fcd85fb2129ed89bbe583263d84840993c9[log] [tgz]
authorLuis Vega <vegaluisjose@users.noreply.github.com>Wed Jun 05 22:03:12 2019 -0700
committereqy <eddieyan101@gmail.com>Wed Jun 05 22:03:12 2019 -0700
tree15f2286d1f11da69219638668afb3079a5f29bfc
parent2f24e4ad2f60f39e7e9fa2c6bf403cf4a4c30157 [diff]
[VTA] [APPS] [TSIM] small naming fix  (#3293)

* make off lowercase

* update README
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tree: 15f2286d1f11da69219638668afb3079a5f29bfc
  1. apps/
  2. config/
  3. hardware/
  4. include/
  5. python/
  6. src/
  7. tests/
  8. tutorials/
  9. 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.