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apache / tvm-vta / 58f070f179c44863899c3339adaf806753dfd13c
commit58f070f179c44863899c3339adaf806753dfd13c[log] [tgz]
authorLuis Vega <vegaluisjose@users.noreply.github.com>Wed May 08 09:59:00 2019 -0700
committerThierry Moreau <moreau@uw.edu>Wed May 08 09:59:00 2019 -0700
treeb557075859a729f73cf5c482a6546c8413784a9b
parent01f124622359b03fe6f17c030393eba35682d1b3 [diff]
[RFC] [VTA] [TSIM] Enabling Cycle-Accurate Hardware Simulation for VTA #3009 (#3010)

* merge files

* move verilator to the right place

* change name to tsim

* add default rule to be build and run

* add README for tsim

* Update README.md

* add some structural feedback

* change name of VTASim to VTADPISim

* more renaming

* update comment

* add license

* fix indentation

* add switch for vta-tsim

* add more licenses

* update readme

* address some of the new feedback

* add some feedback from cpplint

* add one more whitespace

* pass pointer so linter is happy

* pass pointer so linter is happy

* README moved to vta documentation

* create types for dpi functions, so they can be handle easily

* fix pointer style

* add feedback from docs

* parametrize width data and pointers

* fix comments

* fix comment

* add comment to class

* add missing parameters

* move README back to tsim example

* add feedback

* add more comments and remove un-necessary argument in finish

* update comments

* fix cpplint

* fix doc
35 files changed
tree: b557075859a729f73cf5c482a6546c8413784a9b
  1. apps/
  2. config/
  3. hardware/
  4. include/
  5. python/
  6. src/
  7. tests/
  8. tutorials/
  9. 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.