Better details on the about page

doc/sphinx/source/conf.py

@@ -39,6 +39,7 @@ extensions = ['sphinx.ext.mathjax',
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
+static_path = ['static']
 
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:

doc/sphinx/source/howto/index.rst

@@ -38,13 +38,13 @@ devices, or it might mean programatically adjusting a model or the compute
 resources it requires, at an unknown or arbitray time after it has been deemed 
 to be trained well enough.
 
-To get started, we've provided a basic example for how to :doc:`execute` a 
-computation with an nGraph backend; this is analogous to a framework bridge.
+To get started, we've provided a basic example for how to :doc:`execute` with 
+an nGraph backend; this is analogous to a framework bridge.
 
 For data scientists or algorithm developers who are trying to extract specifics 
 about the state of a model at a certain node, or who want to optimize a model 
-at a more granular level, we provide an example for how to :doc:`import` a 
-model and run inference after it has been exported from a DL framework.    
+at a more granular level, we provide an example for how to :doc:`import` and 
+run inference after it has been exported from a DL framework.    
 
 This section is under development; we'll continually populate it with more 
 articles geared toward data scientists, algorithm designers, framework developers, 

doc/sphinx/source/index.rst

@@ -22,7 +22,7 @@ project enables modern compute platforms to run and train :abbr:`Deep Neural Net
 models. It is framework neutral and supports a variety of backends used by 
 :abbr:`Deep Learning (DL)` frameworks. 
 
-.. image:: graphics/ngraph-ecosys.png
+.. image:: ../static/ngraph-ecosystem.png
    :width: 585px
 
 For this early release, we've provided :doc:`framework-integration-guides` to 

doc/sphinx/source/project/about.rst

@@ -3,19 +3,39 @@
 About
 =====
 
-Welcome to Intel® nGraph™, an open source C++ library and compiler. This 
-project enables modern compute platforms to run and train 
-:abbr:`Deep Neural Network (DNN)` models. It is framework-neutral and supports 
-a variety of backends used by :abbr:`Deep Learning (DL)` frameworks. 
+Welcome to nGraph™, an open-source C++ compiler library for running and 
+training :abbr:`Deep Neural Network (DNN)` models. This project is 
+framework-neutral and can target a variety of modern devices or platforms. 
 
-.. figure:: ../graphics/ngraph-ecosys.png
+.. figure:: ../graphics/ngraph-ecosystem.png
    :width: 585px	 
- 
-The nGraph library translates a framework’s representation of computations into 
-an :abbr:`Intermediate Representation (IR)` designed to promote computational 
-efficiency on target hardware. Initially-supported backends include Intel 
-Architecture CPUs, the Intel® Nervana Neural Network Processor™ (NNP), 
-and NVIDIA\* GPUs. 
+  
+nGraph currently supports :doc:`three of the most popular <framework-integration-guides>` 
+frameworks for :abbr:`Deep Learning (DL)` models. through what we call 
+a :term:`bridge` that can be integrated during the framework's build time. 
+For developers working with other frameworks (even those not listed above), 
+we've created a :doc:`howto/index` guide so you can teach yourself how to 
+create bridge code that can be used to :doc:`howto/execute`.
+
+With nGraph, data scientists can focus on data science rather than worrying 
+about how to adapt models to train and run efficiently on different devices.
+We've recently added initial support for the `ONNX`_ format. Developers who already have a "trained" model can use nGraph to bypass a lot 
+of the framework-based complexity and :doc:`howto/import` to test or run it 
+on targeted and efficient backends with our user-friendly ``ngraph_api``. 
+
+Supported platforms
+--------------------
+
+Initially-supported backends include 
+
+* Intel Architecture CPUs, 
+* the Intel® Nervana Neural Network Processor™ (NNP), and 
+* NVIDIA\* CUDA GPUs. 
+
+Tentaively in the pipeline, we'll be adding backend support for
+
+* :abbr:`Field Programmable Gate Arrays (FPGA)`
+* Movidius compute stick
 
 
 Why was this needed?
@@ -90,6 +110,7 @@ our `arXiv paper`_ from the 2018 SysML conference.
 
 
 .. _widely-supported frameworks: http://ngraph.nervanasys.com/docs/latest/framework-integration-guides.html
+.. _ONNX: http://onnx.ai 
 .. _arXiv paper: https://arxiv.org/pdf/1801.08058.pdf
 .. _Intel® MKL-DNN: https://github.com/intel/mkl-dnn