Google Git
Sign in
apache / tvm-vta / 255d9b2712d6b9ba72a73a08df7b7ba6f58b8814
commit255d9b2712d6b9ba72a73a08df7b7ba6f58b8814[log] [tgz]
authorThierry Moreau <moreau@uw.edu>Tue Jul 30 14:01:31 2019 -0700
committerJared Roesch <roeschinc@gmail.com>Tue Jul 30 14:01:31 2019 -0700
treefc54061b02fcea25435ff1df3a3b1aca0d134eaa
parentcffabb09e47383b14841ddefb3dc4753775ff429 [diff]
[VTA] Support for batched inference (#3661)

* fix in IR pass to support padding on 6-d tensors

* support for both N>1 and N==1 for padding

* batch size > 1 tuning and base config

* output formatting

* batch conv2d

* print all category results

* revert to single-batch config

* pick record best

* fix conv test

* improving reporting

* address batching bug in fast simulator

* fix
6 files changed
tree: fc54061b02fcea25435ff1df3a3b1aca0d134eaa
  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.