[incubator-tvm-site] branch master updated: Add history

[tq...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

tqchen pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm-site.git


The following commit(s) were added to refs/heads/master by this push:
     new 8fc6517  Add history
8fc6517 is described below

commit 8fc6517f339514bc4a901cb58f4688b841d932f0
Author: tqchen <tq...@gmail.com>
AuthorDate: Sun Sep 27 15:43:39 2020 -0700

    Add history
---
 history.md | 15 +++++++++++++++
 index.md   |  6 ++++--
 2 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/history.md b/history.md
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+---
+layout: page
+title: "History"
+---
+# History
+
+TVM began as a research project at the [SAMPL group](https://sampl.cs.washington.edu/) of
+Paul G. Allen School of Computer Science & Engineering, University of Washington.
+The project is now an effort undergoing incubation at The Apache Software Foundation (ASF),
+driven by an open source community involving multiple industry and academic institutions
+under the Apache way.
+
+TVM provides two level optimizations show in the following figure.
+Computational graph optimization to perform tasks such as high-level operator fusion, layout transformation, and memory management.
+Then a tensor operator optimization and code generation layer that optimizes tensor operators. More details can be found at the [techreport](https://arxiv.org/abs/1802.04799).
diff --git a/index.md b/index.md
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+++ b/index.md
@@ -12,8 +12,10 @@ layout: index
     ![responsiveAbout](/assets/images/about-responsive-image.svg "responsiveAbout"){:.responsiveImg}
 * {:.aboutDetailsCol}
 #### About Apache TVM
-Apache TVM is an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It aims to close the gap between the productivity-focused deep learning frameworks,
-and the performance- or efficiency-oriented hardware backends. TVM provides the following main features:
+The vision of the Apache TVM Project is to host a diverse community of experts and practitioners
+in machine learning, compilers, and systems architecture to build an accessible, extensible, and
+automated open-source framework that optimizes current and emerging machine learning models for
+any hardware platform. TVM provides the following main features:
 
     * Compilation of deep learning models into minimum deployable modules.
     * Infrastructure to automatic generate and optimize models on more backend with better performance.