Google Git
Sign in
apache / tvm-vta / cb9958a62c8e4ae694697205f79a5f6e90e709e8
commitcb9958a62c8e4ae694697205f79a5f6e90e709e8[log] [tgz]
authorHua Jiang <huaj@xilinx.com>Fri Sep 06 17:03:51 2019 -0700
committerThierry Moreau <moreau@uw.edu>Fri Sep 06 17:03:51 2019 -0700
treeebf7ea0af193173b0e7ec8e020ecc89183367523
parent29a0e9a300939c34f233ae0b671c9a27ba5b77c8 [diff]
[VTA] Support TLPP in function simulator. (#3555)

* [VTA] Support TLPP in function simulator.
Issue:
currently vta function simulator just doing serialized instruction
execution, the dependency logic of runtime ISA which use for task
level pipe line parallelism can not get verified by function simulator.

Solution:
make the simulator driver to be multiple thread and support TLPP.

Benefit:
TLPP support VTA function simulator would make VTA logic testing/debug
/change more easy.

replace boost lockfree queue

add configure control for simulator tlpp enable or disable.

change code tyle into google style.

Wrap queue read/write and sync logic to make function call more simple.

Add some comments.

Remove MT logic, change into Single thread mode.

address review comments.

code style change to match google code style and add comments.

add cmake macro to enable/disable simulator tlpp logic.

submodule update.

correct file name mentioned in comments.

* remove USE_VTA_FSIM_TLPP.
3 files changed
tree: ebf7ea0af193173b0e7ec8e020ecc89183367523
  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.