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apache / tvm-vta / 4f7d2497c1655354f23c108d120c4ba030244d80
commit4f7d2497c1655354f23c108d120c4ba030244d80[log] [tgz]
authorLunderberg <Lunderberg@users.noreply.github.com>Thu Aug 19 15:38:44 2021 -0500
committerGitHub <noreply@github.com>Thu Aug 19 13:38:44 2021 -0700
tree524a093de9ea5a108f0aaea8baa90a078765d702
parentc18a22475d8ccb5447da4591c709d347687fb2b8 [diff]
Renamed graph_runtime references to graph_executor. (#31)

Following TVM's rename in https://github.com/apache/tvm/pull/7653,
update to avoid errors when tvm.contrib.graph_runtime is removed
entirely.
1 file changed
tree: 524a093de9ea5a108f0aaea8baa90a078765d702
  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.