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apache / tvm-vta / 7ffd5c5b33a30df64ad826e2f5490d632ef7f305
commit7ffd5c5b33a30df64ad826e2f5490d632ef7f305[log] [tgz]
authorLeandro Nunes <leandro.nunes@arm.com>Mon Jan 06 16:31:07 2020 +0000
committerTianqi Chen <tqchen@users.noreply.github.com>Mon Jan 06 08:31:07 2020 -0800
tree3bd164b9c3b4f3bd19df71788a2d43d2d37e03a5
parent453d0c87b555afcce8797b8a63e155aa8f92a020 [diff]
Pin python pillow to "<7" due to torchvision 1.2.0 dependency issue (#4632)

* As a result of backwards incompatible changes released in pillow 7.0,
   torchvision crashes if you just "pip install pillow", as we do in
   a few places.

 * This patch sets pillow<7 to be installed in Dockerfiles and support
   material as tutorials and documentation.
2 files changed
tree: 3bd164b9c3b4f3bd19df71788a2d43d2d37e03a5
  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.