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apache / tvm-vta / 495428cdb77493fb94a18a82cc9f26ceda2f65f9
commit495428cdb77493fb94a18a82cc9f26ceda2f65f9[log] [tgz]
authorLiangfu Chen <liangfu.chen@icloud.com>Sun Mar 08 02:42:44 2020 +0800
committerGitHub <noreply@github.com>Sat Mar 07 10:42:44 2020 -0800
tree4d085a4a4ff72dadcae740058b98fe4a76ec1c46
parent28958c621c577f6cd21f5accecaf18126dbeefe2 [diff]
[VTA][Chisel] Change Scala Linter scalafmt => scalastyle (#4998)

* scalafmt => scalastyle

Change-Id: Ifc590e7cb63585f35dfdc9efcf3c6287b1afb1dd

* scalafmt => scalastyle

Change-Id: I8aff2632dadda05d2896e28bdaf6f780a160a15a

* add indentation constraint

Change-Id: Ibeb00c11a5718ea47322ea2b82e757828af8af91

* trigger ci again
40 files changed
tree: 4d085a4a4ff72dadcae740058b98fe4a76ec1c46
  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.