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+## Motivation
+
+There is a ```gap``` between the ```Program``` defined by
+user and the ```Executable``` that can be scheduled
+efficiently on heterogeneous hardware, either locally
+or distributedly.
+
+Usually, the ```gap``` is bridged by
+
+* A serious transformations with defined order.
+
+* The transformations usually invovle
+```insert, delete, clustering, split, dependency analysis```.
+
+* Has a simple way to verify and debug each transformation.
+
+* Flexible to add, remove or customize transformations to fit
+the requirements of various algorithms (models) and hardware secenarios.
+
+Some other events also push us to a better unified pattern.
+
+* The deep learning framework is built around the concepts of graphs.
+To leverage tools such as compilation (e.g. TVM and nGraph) or
+cross-framework conversion (e.g. ONNX), we also need a intermediate
+representation that can be connected to the rest of the ecosystem.
+
+
+We need a unified pattern to naturally support the requirements
+described above. The pattern should fit both training, inference
+and other offline serielized model transformations.
+Learned from LLVM and other deep learning framework, we draft the
+design below.
+
+
+## Design
+
+### Major Concepts
+
+#### Node
+
+```Node``` represents an operation that performs some computation or
+a variable that is input or output of operation.
+
+```Node```s are connected to other ```Node```s via inputs and outputs.
+
+#### Graph
+
+```Graph``` contains a list of ```Node```s.
+
+TODO: Better definitions for the graph.
+
+```Graph``` can also contain ```Attribute```s. ```Attribute```s
+can be ``any`` thing. For example, it can be a list of "wraper"
+nodes. The ```wrapper``` nodes compose ```Node```s and provide
+helper method for execution. ```Attribute``` can also contain
+other things that describe some properties of the ```Graph```.
+
+#### Pass
+
+```Pass``` represents a transformation of ```Graph```. Its input
+is a ```Graph``` and its output is also a ```Graph```. For example,
+a ```Pass``` can simply print out the ```Graph```. A ```Pass```
+can also fuse some ```Graph```'s ```Node```s.
+
+#### Optimize
+
+```Optimize``` contains a series of ```Pass``` with defined order.
+```Optimize``` transforms a ```Graph``` that only contains raw
+modeling logic to a ```Graph``` that can be run efficiently while
+maintaining the original modeling logic.
+
+
+### Workflow
+
+
+* Program is first converted to Graph.
+* Graph goes through a series of Pass
+* Graph is transformed from raw model logic to a
+form that is efficient to execute.
+
+Graph->Pass1->Graph->Pass2->Graph->Pass3->Executor