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apache / tvm-vta / 87ce9acfae550d1a487746e9d06c2e250076e54c
commit87ce9acfae550d1a487746e9d06c2e250076e54c[log] [tgz]
authorLuis Vega <vegaluisjose@users.noreply.github.com>Wed Sep 16 10:24:22 2020 -0700
committerGitHub <noreply@github.com>Wed Sep 16 10:24:22 2020 -0700
tree1d6935656e51c00791d2c8520f576fbd87313741
parent9b178fdb387bffc708f2448a82e85b4737239aed [diff]
[Vivado] Export VTA configuration variables to TCL for hardware builds (#11)

* generate tcl file for hardware tools from vta_config.py

* add comment to function

* remove this
4 files changed
tree: 1d6935656e51c00791d2c8520f576fbd87313741
  1. apps/
  2. config/
  3. hardware/
  4. include/
  5. src/
  6. tests/
  7. .asf.yaml
  8. .gitignore
  9. Jenkinsfile
  10. LICENSE
  11. NOTICE
  12. README.md
README.md

VTA Hardware Design Stack

Build Status

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.