# onnxruntime-tvm

**Repository Path**: luo_zhi_cheng/onnxruntime-tvm

## Basic Information

- **Project Name**: onnxruntime-tvm
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-20
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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<img src=https://raw.githubusercontent.com/tqchen/tvm.ai/master/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack
==============================================
[Documentation](https://docs.tvm.ai) |
[Contributors](CONTRIBUTORS.md) |
[Community](https://tvm.ai/community.html) |
[Release Notes](NEWS.md)

[![Build Status](http://ci.tvm.ai:8080/buildStatus/icon?job=tvm/master)](http://ci.tvm.ai:8080/job/tvm/job/master/)
[![Azure Pipeline](https://dev.azure.com/tvmai/tvm/_apis/build/status/windows_mac_build?branchName=master)](https://dev.azure.com/tvmai/tvm/_build/latest?definitionId=2&branchName=master)

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](https://tvm.ai/)  for more information.

License
-------
© Contributors Licensed under an [Apache-2.0](https://github.com/dmlc/tvm/blob/master/LICENSE) 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](https://docs.tvm.ai/contribute/)

Acknowledgement
---------------
We learned a lot from the following projects when building TVM.
- [Halide](https://github.com/halide/Halide): TVM uses [HalideIR](https://github.com/dmlc/HalideIR) as data structure for
  arithmetic simplification and low level lowering. We also learned and adapted some part of lowering pipeline from Halide.
- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives.
- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.