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apache / tvm-vta / 0816033f93a9290bbdf8817ccfbf2caf99a41a66
commit0816033f93a9290bbdf8817ccfbf2caf99a41a66[log] [tgz]
authorHua Jiang <huaj@xilinx.com>Thu Jun 27 09:38:27 2019 -0700
committerTianqi Chen <tqchen@users.noreply.github.com>Thu Jun 27 09:38:27 2019 -0700
tree2c49c8ced7197e4d363ae109a79584df01c5ebc8
parent5d228cf6b092ef5c1f6f90754ad1909bbf19c21b [diff]
[VTA][TSIM] Verilator compile report error for printf (#3438)

[Symptom]
after follow the tsim example readme, doing verilator install by 'sudo apt-get-install verilator'
Once enable 'debug' or manually add 'printf' logic in chisel module, verilator would report
following error.
'syntax error, unexpected INTEGER NUMBER, expecting IDENTIFIER'

[Fix]
upgrade verilator to 4.012, issue fixed.

[Solution]
Link README.md verilator install steps with verilator home website
install instruction.
1 file changed
tree: 2c49c8ced7197e4d363ae109a79584df01c5ebc8
  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.