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apache / tvm-vta / 270e9be9dec32a29d1313b566862b14b97d81c2d
commit270e9be9dec32a29d1313b566862b14b97d81c2d[log] [tgz]
authorTianqi Chen <tqchen@users.noreply.github.com>Tue Jun 19 20:38:55 2018 -0700
committerTianqi Chen <tqchen@users.noreply.github.com>Wed Jul 11 21:54:39 2018 -0700
tree608cae8591650fdf51722112d2ad8ba2ddf1e763
parent013dd45981219b8d6be56c0a6451dfcb76f80ffe [diff]
Update Jenkinsfile
1 file changed
tree: 608cae8591650fdf51722112d2ad8ba2ddf1e763
  1. apps/
  2. docs/
  3. examples/
  4. hardware/
  5. include/
  6. make/
  7. python/
  8. src/
  9. tests/
  10. tutorials/
  11. CONTRIBUTORS.md
  12. Jenkinsfile
  13. LICENSE
  14. Makefile
  15. NEWS.md
  16. README.md
README.md

VTA: Open, Modular, Deep Learning Accelerator Stack

Build Status GitHub license

VTA(versatile tensor accelerator) is an open-source deep learning accelerator stack. It is not just an open-source hardware, but is an end to end solution that includes the entire software stack on top of VTA open-source hardware.

The key features include:

  • Generic, modular open-source hardware
    • Streamlined workflow to deploy to FPGAs.
    • Simulator support
  • Driver and JIT runtime for both simulated backend and FPGA.
  • End to end TVM stack integration
    • Direct optimization and deploy models from deep learning frameworks via TVM stack.
    • Customized and extendible TVM compiler backend
    • Flexible RPC support to ease the deployment, you can program it with python :)

VTA is part of our effort on TVM Stack.

License

© Contributors, 2018. Licensed under an Apache-2.0 license.