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apache / tvm-vta / 3c233635c13b7344b70b9e7fd9a45ad10d6ab09c
commit3c233635c13b7344b70b9e7fd9a45ad10d6ab09c[log] [tgz]
authorHua <allenhjiang@outlook.com>Fri May 31 19:42:15 2019 -0700
committerThierry Moreau <moreau@uw.edu>Fri May 31 21:42:15 2019 -0500
tree6bfdc239c9d8e040a6d499b18584c1b14ca09d93
parent302637c9113f21b201b0d5d7f96e4c6860476214 [diff]
[Bugfix][VTA] PkgConfig cause crash in PYNQ board due to link library (#3257)

* [Bugfix][VTA] PkgConfig cause crash in PYNQ board due to link library
not exist.

Symptom:
When run vta_get_started.py with pynq board, host crash and
complain "cannot find -lsds_lib" and "cannot find -l:libdma.so"

Reproduce:
At pynq board, delete the ./build/vta_config.json, then run rpc
server.
In host machine run vta_get_started.py, issue would reproduce.

Analysis:
This issue caused by 'PkgConfig' function  still using pynq2.1
library which not exist in pynq2.4 anymore, when a "reconfig_runtime"
logic of rpc_server.py get triggered , the compile would failed due to
link library not exist.

Solution:
change the link library to libcma.so.

* [Document Change][VTA] Change pynq version from 2.3 into 2.4.

Issue:
pynq 2.3 image not available anymore from pynq download page and pynq
2.4 is the current latest image which available in the said website, after
verification, currently VTA work good with pynq 2.4 image, hence update
related document from pynq 2.3 to 2.4.
1 file changed
tree: 6bfdc239c9d8e040a6d499b18584c1b14ca09d93
  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.