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apache / tvm-vta / 48fa54b6ecc3f2cee425401f054a54257fb30537
commit48fa54b6ecc3f2cee425401f054a54257fb30537[log] [tgz]
authorThierry Moreau <tmoreau@octoml.ai>Sat Mar 28 17:26:40 2020 -0700
committerGitHub <noreply@github.com>Sat Mar 28 17:26:40 2020 -0700
treecb197802a4fa9b5b592a7e6b6d9cea2eb553e878
parent66223ef1ee04237485058343a4791991b7aecda8 [diff]
[VTA][Refactor] Introducing VTA_HW_PATH for easier migration (#5163)
11 files changed
tree: cb197802a4fa9b5b592a7e6b6d9cea2eb553e878
  1. python/
  2. runtime/
  3. scripts/
  4. tests/
  5. tutorials/
  6. vta-hw/
  7. 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.