Revamp repo doc (#2560)

* Update gold release date and features

* Diagram that shows FW & HW support for main md

* Simplified nGraph architecture for CPU

* Complex architecture diagram for all backends

* Replace

* Add ngraph stack diagram with FW and HW support

* relocate

* Relocate

* Replace stack diagram with NNP-L & NNP-I

* Updated copy and removed the disclaimer

* Removed "more detailed" sentence

* Update README.md

* Fixed sentence

* Update README.md

* nGraph logo small version

* Update README.md

* Added logo

* nGraph logo smallest version

* nGraph logo with header

* Update README.md

* Delete ngraph_logo_header.png

* nGraph logo with header

* Update README.md

* nGraph header on the main repo page

* Added header

* nGraph architecture simplified for CPU

* nGraph architecture complex

* Added architecture simple architecture diagram

* Modified the full stack diagram

* nGraph architecture simple diagram with padding

* Added padding to simple architecture image

* Update ABOUT.md

* Updated the sentences with NNP full name

README.md

 ![nGraph Compiler stack](doc/sphinx/source/graphics/ngraph_header.png)
-===========================
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/NervanaSystems/ngraph/blob/master/LICENSE) [![Build Status][build-status-badge]][build-status] 
 
 <div align="left">
@@ -49,8 +48,8 @@ providing freedom, performance, and ease-of-use to AI developers.
 
 The diagram below shows deep learning frameworks and hardware targets
 supported by nGraph. NNP-L and NNP-I in the diagram refer to Intel's next generation 
-deep learning accelators: Intel® Nervana™ Neural Network Processor for Learning and 
-Inference. Future plans for supporting addtional deep learning frameworks
+deep learning accelerators: Intel® Nervana™ Neural Network Processor for Learning and 
+Inference respectively.  Future plans for supporting addtional deep learning frameworks 
 and backends are outlined in the [ecosystem] section. 
 
 
@@ -113,5 +112,3 @@ to improve it:
 [nGraph-ONNX adaptable]: https://ai.intel.com/adaptable-deep-learning-solutions-with-ngraph-compiler-and-onnx/
 [nGraph for PyTorch developers]: https://ai.intel.com/investing-in-the-pytorch-developer-community
 [Validated workloads]: https://ngraph.nervanasys.com/docs/latest/frameworks/genre-validation.html
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