Latest illustrations of nG components and features (#3617)

* Latest illustrations of nG components and features

* Update ABOUT.md

* Update full stack diagram

* fix typo

* Updating as per discussion

ABOUT.md

@@ -4,19 +4,20 @@ About nGraph Compiler stack
 nGraph Compiler stack architecture
 ----------------------------------
 
-The diagram below represents our current Beta release stack.
-In the diagram, nGraph components are colored in gray. Please note
+The diagram below represents our current release stack. In the diagram, 
+nGraph components are colored in gray. Please note
 that the stack diagram is simplified to show how nGraph executes deep
 learning workloads with two hardware backends; however, many other
 deep learning frameworks and backends currently are functioning.
 
-![](doc/sphinx/source/graphics/arch_simple_pad.png)
+![](doc/sphinx/source/graphics/ngraph_arch_diag.png)
 
-#### Bridge
+
+## Bridge
 
 Starting from the top of the stack, nGraph receives a computational graph
-from a deep learning framework such as TensorFlow* or MXNet*. The
-computational graph is converted to an nGraph internal representation
+from a deep learning framework such as TensorFlow or MXNet. The
+computational graph is converted to an nGraph internal representation 
 by a bridge created for the corresponding framework.
 
 An nGraph bridge examines the whole graph to pattern match subgraphs
@@ -24,7 +25,8 @@ which nGraph knows how to execute, and these subgraphs are encapsulated.
 Parts of the graph that are not encapsulated will default to framework
 implementation when executed.
 
-#### nGraph Core
+
+## nGraph Core
 
 nGraph uses a strongly-typed and platform-neutral
 `Intermediate Representation (IR)` to construct a "stateless"
@@ -38,10 +40,11 @@ memory management, data layouts, etc.
 
 In addition, using nGraph IR allows faster optimization delivery
 for many of the supported frameworks. For example, if nGraph optimizes
-ResNet* for TensorFlow*, the same optimization can be readily applied
-to MXNet* or ONNX* implementations of ResNet*.
+ResNet for TensorFlow, the same optimization can be readily applied
+to MXNet* or ONNX* implementations of ResNet.
+
 
-#### Hybrid Transformer
+## Hybrid Transformer
 
 Hybrid transformer takes the nGraph IR, and partitions it into
 subgraphs, which can then be assigned to the best-performing backend.
@@ -57,7 +60,7 @@ Once the subgraphs are assigned, the corresponding backend will
 execute the IR.
 
 
-#### Backends
+## Backends
 
 Focusing our attention on the CPU backend, when the IR is passed to
 the Intel® Architecture (IA) transformer, it can be executed in two modes:
@@ -70,10 +73,7 @@ nGraph leverages existing kernel libraries such as MKL-DNN, Eigen, and MLSL.
 
 MLSL library is called when nGraph executes distributed training.
 At the time of the nGraph Beta release, nGraph achieved state of the art
-results for ResNet50 with 16 nodes and 32 nodes for TensorFlow* and MXNet*.
-We are excited to continue our work in enabling distributed training,
-and we plan to expand to 256 nodes in Q4 ‘18. Additionally, we
-are testing model parallelism in addition to data parallelism.
+results for ResNet50 with 16 nodes and 32 nodes for TensorFlow and MXNet.
 
 The other mode of execution is Direct EXecution (DEX). In DEX mode,
 nGraph can execute the operations by directly calling associated kernels
@@ -106,23 +106,16 @@ non-device-specific optimizations:
 -   **Memory management** -- Prevent peak memory usage by intercepting
     a graph with or by a "saved checkpoint," and to enable data auditing.
 
-Beta Limitations
-----------------
+Limitations
+-----------
+
+The Beta release of nGraph only supports Just-In-Time (JiT) compilation; 
+Ahead-of Time (AoT) compilation will be supported in the official release. 
+nGraph currently has limited support for dynamic shapes.
 
-In this Beta release, nGraph only supports Just In Time compilation,
-but we plan to add support for Ahead of Time compilation in the official
-release of nGraph. nGraph currently has limited support for dynamic graphs.
 
 Current nGraph Compiler full stack
 ----------------------------------
 
-![](doc/sphinx/source/graphics/arch_complex.png)
-
+![](doc/sphinx/source/graphics/ngraph_full_stack_diagrams.png)
 
-In addition to IA and NNP transformers, nGraph Compiler stack has transformers
-for multiple GPU types and an upcoming Intel deep learning accelerator. To
-support the growing number of transformers, we plan to expand the capabilities
-of the hybrid transformer with a cost model and memory sharing. With these new
-features, even if nGraph has multiple backends targeting the same hardware, it
-will partition the graph into multiple subgraphs and determine the best way to
-execute each subgraph.

README.md

@@ -36,7 +36,7 @@ seen performance boosts running workloads that are not included on the list of
 Additionally we have integrated nGraph with [PlaidML] to provide deep learning 
 performance acceleration on Intel, nVidia, & AMD GPUs. More details on current 
 architecture of the nGraph Compiler stack can be found in [Architecture and features],
-and recent changes to the stack are explained in [Release Notes].
+and recent changes to the stack are explained in the [Release Notes].
 
 ## What is nGraph Compiler? 
 
@@ -50,15 +50,9 @@ deep learning accelerators: Intel® Nervana™ Neural Network Processor for Lear
 Inference respectively.  Future plans for supporting addtional deep learning frameworks 
 and backends are outlined in the [ecosystem] section. 
 
-![](doc/sphinx/source/graphics/ngpipelines.png)
+![](doc/sphinx/source/graphics/nGraph_main.png)
 
 
-While the ecosystem shown above is all functioning, we have validated 
-performance for deep learning inference on CPU processors, such as Intel® Xeon® 
-for the Beta release of nGraph. The Gold release is targeted for June 2019; it 
-will feature broader workload coverage including quantized graphs (int8) and 
-will implement support for dynamic shapes. 
-
 Our documentation has extensive information about how to use nGraph Compiler 
 stack to create an nGraph computational graph, integrate custom frameworks, 
 and to interact with supported backends. If you wish to contribute to the 
@@ -77,9 +71,10 @@ to improve it:
 * In the case of a larger feature, create a test.
 * Submit a [pull request].
 * Make sure your PR passes all CI tests. Note: You can test locally with `make check`.
-* We will review your contribution and, if any additional fixes or
-  modifications are necessary, may provide feedback to guide you. When
-  accepted, your pull request will be merged to the repository.
+
+ We will review your contribution and, if any additional fixes or modifications are 
+ necessary, may provide feedback to guide you. When accepted, your pull request will 
+ be merged to the repository.
 
 
 [Ecosystem]: ./ecosystem-overview.md
@@ -96,7 +91,7 @@ to improve it:
 [contrib guide]: https://www.ngraph.ai/documentation/contributing/guide
 [pull request]: https://github.com/NervanaSystems/ngraph/pulls
 [how to import]: https://www.ngraph.ai/tutorials/onnx-tutorial#import-a-model-with-onnx-and-ngraph
-[ngraph_wireframes_with_notice]: doc/sphinx/source/graphics/ngpipelines.png "nGraph wireframe"
+[ngraph_wireframes_with_notice]: doc/sphinx/source/graphics/nGraph_main.png "nGraph components"
 [build-status]: https://travis-ci.org/NervanaSystems/ngraph/branches
 [build-status-badge]: https://travis-ci.org/NervanaSystems/ngraph.svg?branch=master
 [PlaidML]: https://github.com/plaidml/plaidml