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apache / tvm-vta / 5d228cf6b092ef5c1f6f90754ad1909bbf19c21b
commit5d228cf6b092ef5c1f6f90754ad1909bbf19c21b[log] [tgz]
authorHua Jiang <huaj@xilinx.com>Tue Jun 25 00:26:47 2019 -0700
committerThierry Moreau <moreau@cs.washington.edu>Tue Jun 25 00:26:47 2019 -0700
treef0af896a629f78516f06876e7b7761ec4c35b534
parent1ac62131945204cfd32884e512f6550c5466ad87 [diff]
[VTA] Add VTA PYNQ metal_test bitstream program logic and fix compile issue. (#3400)

* [VTA] Add VTA PYNQ metal_test bitstream program logic and fix couple compile issue.

Issue:
VTAProgram not exist and cause compile error.
No logic to program the bitstream into FPGA.
metal test still use pynq 2.1 library which not support on latest
pynq 2.4.

Solution:
remove old VTAProgram.
when setting is pynq, program the bitstream during compile.
change DMA link library to libcma.

* Address review commends.
4 files changed
tree: f0af896a629f78516f06876e7b7761ec4c35b534
  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.