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Hzfengsy merged 1 commit into from
Jan 26, 2025
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[DOCS] Update README #17604

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24 changes: 17 additions & 7 deletions README.md
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Expand Up @@ -22,9 +22,6 @@
[Community](https://tvm.apache.org/community) |
[Release Notes](NEWS.md)

[![Build Status](https://ci.tlcpack.ai/buildStatus/icon?job=tvm/main)](https://ci.tlcpack.ai/job/tvm/job/main/)
[![WinMacBuild](https://github.com/apache/tvm/workflows/WinMacBuild/badge.svg)](https://github.com/apache/tvm/actions?query=workflow%3AWinMacBuild)

Apache 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.
Expand All @@ -44,10 +41,23 @@ Contribute to TVM
TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community.
Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).

Acknowledgement
---------------
We learned a lot from the following projects when building TVM.
History and Acknowledgement
---------------------------
TVM started as a research project for deep learning compiler.
The first version of the project benefited a lot from following projects:

- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module
originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.
originates from Halide. 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.

Since then, the project has gone through several rounds of redesigns.
The current design is also drastically different from the initial design, following the
development trend of ML compiler community.

The most recent version focuses on a cross-level design with TensorIR as tensor-level representation
and Relax as graph level representation, and python-first transformations.
The current design goal of the project is to make the ML compiler accessible by enabling most
transformations to be customizable in Python and bringing a cross-level representation that can jointly
optimize computational graphs, tensor programs, and libraries. The project also serves as a foundation
infra to build python-first vertical compilers for various domains, such as LLMs.
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