update to PR#660

README.md

@@ -5,20 +5,16 @@ compiler. This project enables modern compute platforms to run and
 train Deep Neural Network (DNN) models. It is framework-neutral and
 supports a variety of backends used by Deep Learning (DL) frameworks.
 
-The nGraph library translates a framework’s representation of
-computations into an 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.  Currently-supported compiler optimizations include efficient
-memory management and data layout abstraction.
+![nGraph ecosystem][ngraph-ecosystem]
 
 ## Documentation
 
 See our [install] docs for how to get started. 
 
-For this early release, we provide [framework integration guides] to compile 
-MXNet and TensorFlow-based projects.  
+For this early release, we provide [framework integration guides] to
+compile MXNet and TensorFlow-based projects.  If you already have a
+trained model, we've put together a getting started guide for [how to
+import] a deep learning model and start working with the nGraph APIs.
 
 ## Support
 
@@ -42,3 +38,5 @@ to improve the library:
 [framework integration guides]: http://ngraph.nervanasys.com/docs/latest/framework-integration-guides.html
 [Github issues]: https://github.com/NervanaSystems/ngraph/issues
 [pull request]: https://github.com/NervanaSystems/ngraph/pulls
+[ngraph-ecosystem]: http://ngraph.nervanasys.com/index.html/_images/ngraph-ecosystem.png "nGraph Ecosystem"
+[ngraph-ecosystem]: http://ngraph.nervanasys.com/index.html/_images/ngraph-ecosystem.png "nGraph Ecosystem"
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