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apache / tvm / refs/heads/v0.5
commitf08015e7fde92c835907d4c9b7ad6d3f634e94a5[log] [tgz]
authorJared Roesch <roeschinc@gmail.com>Mon Feb 18 14:18:57 2019 -0800
committerZihengJiang <ziheng@apache.org>Mon Feb 18 14:42:45 2019 -0800
treece7427e59b44b8da70e548ef2e097906e75667f3
parent3d53ef3cfb93fe595465564d161d85fd13e7ee45 [diff]
Fix issue mutating if expressions (#2601)


(cherry picked from commit f6be4d69703afac399072f36b8e000ebbe034f6b)
1 file changed
tree: ce7427e59b44b8da70e548ef2e097906e75667f3
  1. .github/
  2. 3rdparty/
  3. apps/
  4. cmake/
  5. conda/
  6. docker/
  7. docs/
  8. golang/
  9. include/
  10. jvm/
  11. nnvm/
  12. python/
  13. rust/
  14. src/
  15. tests/
  16. topi/
  17. tutorials/
  18. verilog/
  19. vta/
  20. web/
  21. .clang-format
  22. .gitignore
  23. .gitmodules
  24. .travis.yml
  25. CMakeLists.txt
  26. CONTRIBUTORS.md
  27. Jenkinsfile
  28. LICENSE
  29. Makefile
  30. NEWS.md
  31. NOTICE
  32. README.md
  33. version.py
README.md

Open Deep Learning Compiler Stack

GitHub license Build Status

Documentation | Contributors | Community | Release Notes

TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends. Checkout the tvm stack homepage for more information.

License

© Contributors Licensed under an Apache-2.0 license.

Contribute to TVM

TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Checkout the Contributor Guide

Acknowledgement

We learnt a lot from the following projects when building TVM.

  • Halide: TVM uses HalideIR as data structure for arithmetic simplification and low level lowering. We also learnt and adapted some part of lowering pipeline from Halide.
  • Loopy: use of integer set analysis and its loop transformation primitives.
  • Theano: the design inspiration of symbolic scan operator for recurrence.