WIP: Add how_to readme to install tvm with nnpack support (#610)

* feat(docs) add how_to for tvm install with nnpack support

* feat(docs) change python package paragraph

* feat(doc) remove unsure sentence

* add comments on nnpack usage vs TVM

* remove mxnet nnpack tips for nthread change

docs/how_to/nnpack.md

+### NNPACK for Multi-Core CPU Support in TVM
+[NNPACK](https://github.com/Maratyszcza/NNPACK) is an acceleration package
+for neural network computations, which can run on x86-64, ARMv7, or ARM64 architecture CPUs.
+Using NNPACK, higher-level libraries like _MXNet_ can speed up
+the execution on multi-core CPU computers, including laptops and mobile devices.
+
+***Note***: AS TVM already has natively tuned schedules, NNPACK is here mainly for reference and comparison purpose. 
+For regular use prefer native tuned TVM implementation.
+
+_TVM_ supports NNPACK for forward propagation (inference only) in convolution, max-pooling, and fully-connected layers.
+In this document, we give a high level overview of how to use NNPACK with _TVM_.
+
+### Conditions
+The underlying implementation of NNPACK utilizes several acceleration methods,
+including [fft](https://arxiv.org/abs/1312.5851) and [winograd](https://arxiv.org/abs/1509.09308).
+These algorithms work better on some special `batch size`, `kernel size`, and `stride` settings than on other,
+so depending on the context, not all convolution, max-pooling, or fully-connected layers can be powered by NNPACK.
+When favorable conditions for running NNPACKS are not met,
+
+NNPACK only supports Linux and OS X systems. Windows is not supported at present.
+The following table explains under which conditions NNPACK will work.
+
+| operation      | conditions |
+|:---------      |:---------- |
+|convolution     |2d convolution `and` no-bias=False `and` dilate=(1,1) `and` num_group=1 `and` batch-size = 1 or batch-size > 1 && stride = (1,1);|
+|pooling         | max-pooling `and` kernel=(2,2) `and` stride=(2,2) `and` pooling_convention=full    |
+|fully-connected| without any restrictions |
+
+### Build/Install LLVM
+LLVM is required for CPU codegen that needs LLVM.
+Since LLVM takes long time to build from source, you can download pre-built version of LLVM from [LLVM Download Page](http://releases.llvm.org/download.html).
+For llvm 4.0 you can do the following step : 
+
+```bash
+# Add llvm repository in apt source list
+echo "deb http://apt.llvm.org/xenial/ llvm-toolchain-xenial-4.0 main" >> /etc/apt/sources.list
+
+# Update apt source list
+apt-get update
+# Install clang and full llvm
+apt-get install -y \
+    clang-4.0 \
+    clang-4.0-doc \
+    libclang-common-4.0-dev \
+    libclang-4.0-dev \
+    libclang1-4.0 \
+    libclang1-4.0-dbg \
+    libllvm-4.0-ocaml-dev \
+    libllvm4.0 \
+    libllvm4.0-dbg \
+    lldb-4.0 \
+    llvm-4.0 \
+    llvm-4.0-dev \
+    llvm-4.0-doc \
+    llvm-4.0-examples \
+    llvm-4.0-runtime \
+    clang-format-4.0 \
+    python-clang-4.0 \
+    libfuzzer-4.0-dev
+```
+
+### Build/Install NNPACK
+
+If the trained model meets some conditions of using NNPACK,
+you can build TVM with NNPACK support.
+Follow these simple steps:  
+* Build NNPACK shared library with the following commands. _TVM_ will link NNPACK dynamically.
+
+Note: The following NNPACK installation instructions have been tested on Ubuntu 16.04.
+
+#### Build [Ninja](https://ninja-build.org/)
+
+NNPACK need a recent version of Ninja. So we need to install ninja from source.
+```bash
+git clone git://github.com/ninja-build/ninja.git
+cd ninja
+./configure.py --bootstrap
+```
+
+Set the environment variable PATH to tell bash where to find the ninja executable. For example, assume we cloned ninja on the home directory ~. then we can added the following line in ~/.bashrc. 
+```bash
+export PATH="${PATH}:~/ninja"
+```
+
+#### Build [NNPACK](https://github.com/Maratyszcza/NNPACK)
+
+The new CMAKE version of NNPACK download [Peach](https://github.com/Maratyszcza/PeachPy) and other dependencies alone
+
+```bash
+git clone --recursive https://github.com/Maratyszcza/NNPACK.git
+cd NNPACK
+# Add PIC option in CFLAG and CXXFLAG to build NNPACK shared library
+sed -i "s|gnu99|gnu99 -fPIC|g" CMakeLists.txt
+sed -i "s|gnu++11|gnu++11 -fPIC|g" CMakeLists.txt
+mkdir build
+cd build
+# Generate ninja build rule and add shared library in configuration
+cmake -G Ninja -D BUILD_SHARED_LIBS=ON ..
+ninja
+sudo ninja install
+
+# Add NNPACK lib folder in your ldconfig
+echo "/usr/local/lib" > /etc/ld.so.conf.d/nnpack.conf
+sudo ldconfig
+```
+
+### Build TVM with NNPACK support
+
+```bash
+git clone --recursive https://github.com/dmlc/tvm
+```
+
+* Set `USE_NNPACK = 1` in config.mk.
+* Set `NNPACK_PATH` to the $(YOUR_NNPACK_INSTALL_PATH)
+* Set `LLVM_CONFIG = llvm-config-4.0` depending of llvm version installed
+
+after configuration use `make` to build TVM
+
+```bash
+make
+make install
+```
+
+#### Python Package Installation
+
+The python package for [tvm](https://github.com/dmlc/tvm) depends of [topi](https://github.com/dmlc/tvm/tree/master/topi).
+The tvm python package is located at `tvm/python` and topi python package is located in `tvm/topi/python` folder.
+There are several ways to install the package, in all these cases the TVM library and TOPI must be present in the python env:
+
+1. Set the environment variable PYTHONPATH to tell python where to find the libraries. For example, assume we cloned tvm on the home directory ~. then we can added the following line in ~/.bashrc. It is recommended for developers who may change the codes. The changes will be immediately reflected once you pulled the code and rebuild the project (no need to call setup again)
+
+```bash
+export PYTHONPATH=/path/to/tvm/python:/path/to/tvm/topi/python:${PYTHONPATH}
+```
+
+2. Install tvm and topi python bindings by setup.py:
+
+```bash
+# install tvm package for the current user
+cd topi/python
+python setup.py install --user; 
+cd ../../python
+python setup.py install --user; 
+```