Update docs

README.md

-# MiAI Compute Engine
+# Mobile AI Compute Engine
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
 [![build status](http://v9.git.n.xiaomi.com/deep-computing/mace/badges/master/build.svg)](http://v9.git.n.xiaomi.com/deep-computing/mace/pipelines)
 
 [Documentation](docs) |
 [FAQ](docs/faq.md) |
 [Release Notes](RELEASE.md) |
-[MiAI Model Zoo](http://v9.git.n.xiaomi.com/deep-computing/mace-models) |
-[Demo](mace/android) |
+[MACE Model Zoo](https://github.com/XiaoMi/mace-models) |
+[Demo](mace/examples/android) |
 [中文](README_zh.md)
 
-**MiAI Compute Engine** (or **MACE** for short) is a deep learning inference framework optimized for
+**Mobile AI Compute Engine** (or **MACE** for short) is a deep learning inference framework optimized for
 mobile heterogeneous computing platforms. The design is focused on the following
 targets:
 * Performance
@@ -43,7 +43,7 @@ targets:
 * [Create a model deployment file](docs/getting_started/create_a_model_deployment.rst)
 
 ## Performance
-[MiAI Compute Engine Model Zoo](http://v9.git.n.xiaomi.com/deep-computing/mace-models) contains
+[MACE Model Zoo](https://github.com/XiaoMi/mace-models) contains
 several common neural networks models and built daily against a list of mobile
 phones. The benchmark result can be found in the CI result page.
 
@@ -63,7 +63,7 @@ please refer to [the contribution guide](docs/development/contributing.md).
 [Apache License 2.0](LICENSE).
 
 ## Acknowledgement
-MiAI Compute Engine depends on several open source projects located in
+MACE depends on several open source projects located in
 [third_party](third_party) directory. Particularly, we learned a lot from
 the following projects during the development:
 * [Qualcomm Hexagon NN Offload Framework](https://source.codeaurora.org/quic/hexagon_nn/nnlib): the Hexagon DSP runtime

README_zh.md

-# MiAI计算引擎
+# MACE - 移动人工智能计算引擎
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
 [![build status](http://v9.git.n.xiaomi.com/deep-computing/mace/badges/master/build.svg)](http://v9.git.n.xiaomi.com/deep-computing/mace/pipelines)
 
 [文档](docs) |
 [FAQ](docs/faq.md) |
 [发布记录](RELEASE.md) |
-[MiAI Model Zoo](http://v9.git.n.xiaomi.com/deep-computing/mace-models) |
-[Demo](mace/android) |
+[MACE Model Zoo](https://github.com/XiaoMi/mace-models) |
+[Demo](mace/examples/android) |
 [English](README.md)
 
-**MiAI Compute Engine** 是一个专为移动端异构计算平台优化的神经网络计算框架。
+**Mobile AI Compute Engine (MACE)** 是一个专为移动端异构计算平台优化的神经网络计算框架。
 主要从以下的角度做了专门的优化：
 * 性能
   * 代码经过NEON指令，OpenCL以及Hexagon HVX专门优化，并且采用
@@ -35,7 +35,7 @@
 * [如何构建](docs/getting_started/how_to_build.rst)
 
 ## 性能评测
-[MiAI Model Zoo](http://v9.git.n.xiaomi.com/deep-computing/mace-models)
+[MACE Model Zoo](https://github.com/XiaoMi/mace-models)
 包含若干常用模型，并且会对一组手机进行每日构建。最新的性能评测结果可以从项目的持续集成页面获取。
 
 ## 交流与反馈

docs/conf.py

@@ -6,7 +6,7 @@
 import recommonmark.parser
 import sphinx_rtd_theme
 
-project = u'MiAI Compute Engine'
+project = u'Mobile AI Compute Engine (MACE)'
 author = u'%s Developers' % project
 copyright = u'2018, %s' % author
 

docs/getting_started/how_to_build.rst

@@ -19,7 +19,7 @@ Supported Platforms
 Environment Requirement
 -------------------------
 
-MiAI Compute Engine requires the following dependencies:
+MACE requires the following dependencies:
 
 .. list-table::
     :widths: auto
@@ -67,7 +67,7 @@ MiAI Compute Engine requires the following dependencies:
 
     ``export ANDROID_NDK_HOME=/path/to/ndk`` to specify ANDROID_NDK_HOME
 
-MiAI Compute Engine provides Dockerfile with these dependencies installed,
+MACE provides Dockerfile with these dependencies installed,
 you can build the image from the Dockerfile,
 
 .. code:: sh
@@ -95,7 +95,7 @@ Usage
 --------
 
 =======================================
-1. Pull MiAI Compute Engine source code
+1. Pull MACE source code
 =======================================
 
 .. code:: sh
@@ -166,7 +166,7 @@ optimizations for different runtimes,
 
 -  Caffe
 
-MiAI Compute Engine converter only supports Caffe 1.0+, you need to upgrade
+MACE converter only supports Caffe 1.0+, you need to upgrade
 your models with Caffe built-in tool when necessary,
 
 .. code:: bash
@@ -184,7 +184,7 @@ your models with Caffe built-in tool when necessary,
 -----------------
 3.1 Overview
 -----------------
-MiAI Compute Engine can build either static or shared library (which is
+MACE can build either static or shared library (which is
 specified by ``linkshared`` in YAML model deployment file).
 The followings are two use cases.
 
@@ -208,7 +208,7 @@ The followings are two use cases.
          There will be around of 1 ~ 10% performance drop for GPU
          runtime compared to the well tuned library.
 
-MiAI Compute Engine provide command line tool (``tools/converter.py``) for
+MACE provide command line tool (``tools/converter.py``) for
 model conversion, compiling, test run, benchmark and correctness validation.
 
 .. note::

docs/getting_started/introduction.rst

 Introduction
 ============
 
-MiAI Compute Engine is a deep learning inference framework optimized for
+Mobile AI Compute Engine (MACE) is a deep learning inference framework optimized for
 mobile heterogeneous computing platforms. The following figure shows the
 overall architecture.
 
@@ -12,8 +12,8 @@ overall architecture.
 Model format
 ------------
 
-MiAI Compute Engine defines a customized model format which is similar to
-Caffe2. The MiAI model can be converted from exported models by TensorFlow
+MACE defines a customized model format which is similar to
+Caffe2. The MACE model can be converted from exported models by TensorFlow
 and Caffe. A YAML file is used to describe the model deployment details. In the
 next chapter, there is a detailed guide showing how to create this YAML file.
 
@@ -26,7 +26,7 @@ more frameworks will be supported in the future.
 Model loading
 -------------
 
-The MiAI model format contains two parts: the model graph definition and
+The MACE model format contains two parts: the model graph definition and
 the model parameter tensors. The graph part utilizes Protocol Buffers
 for serialization. All the model parameter tensors are concatenated
 together into a continuous byte array, and we call this array tensor data in

docs/index.rst

-MiAI Compute Engine Documentation
-=================================
-Welcome to MiAI Compute Engine documentation.
+Mobile AI Compute Engine Documentation
+======================================
+Welcome to Mobile AI Compute Engine documentation.
 
 The main documentation is organized into the following sections: