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apache / tvm-vta / 3455ef319efe8c0c1377d91e5914164af3e7b30d
commit3455ef319efe8c0c1377d91e5914164af3e7b30d[log] [tgz]
authorHua Jiang <huaj@xilinx.com>Wed Jun 19 20:23:50 2019 -0700
committerThierry Moreau <moreau@uw.edu>Wed Jun 19 20:23:50 2019 -0700
tree3258c50dc5007b8dcffcbe6b6f53e625c4165076
parent453b73c7f240fe320231822273ef865625a00ae1 [diff]
[VTA] Fix VTA function Vivado Compile Error. (#3375)

Issue:
when using vivado compile vta.cc with top function 'vta', vivado
report deadlock error like '...with default size is used in a non -dataflow
region, which may result in deadlock Please consider to resize the
stream using the directive ‘set_directive_stream’ or the ‘HL S stream’
pragma.'

Solution:
give the queue a default size as 8.
2 files changed
tree: 3258c50dc5007b8dcffcbe6b6f53e625c4165076
  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.