fix on_device inference md

tutorials/source_en/advanced_use/on_device_inference.md

@@ -15,7 +15,19 @@
 
 ## Overview
 
-MindSpore Predict is a lightweight deep neural network inference engine that provides the inference function for models trained by MindSpore on the device side. This tutorial describes how to use and compile MindSpore Predict.
+MindSpore Lite is a lightweight deep neural network inference engine that provides the inference function for models trained by MindSpore on the device side. This tutorial describes how to use and compile MindSpore Lite.
+
+![](./images/on_device_inference_frame.png)
+
+Figure 1 On-device inference frame diagram
+
+Mindspore Lite's framework consists of frontend, IR, backend, Lite, RT and micro.
+
+- Frontend: It used for model generation. Users can  use the model building interface to build models, or transform third-party models into mindspore models.
+- IR: It includes the tensor definition, operator prototype definition and graph definition of mindspore. The back-end optimization is based on IR.
+- Backend: Graph optimization and high level optimization are independent of hardware, such as operator fusion and constant folding, while low level optimization is related to hardware; quantization includes weight quantization, activation value quantization and other post training quantization methods.
+- Lite RT: In the inference runtime, session provides the external interface, kernel registry is operator registry, scheduler is operator heterogeneous scheduler and executor is operator executor. Lite RT and shares with Micro the underlying infrastructure layers such as operator library, memory allocation, runtime thread pool and parallel primitives.
+- Micro: Code Gen generates .c files according to the model, and infrastructure such as the underlying operator library is shared with Lite RT.
 
 ## Compilation Method
 
@@ -92,7 +104,7 @@ To perform on-device model inference using MindSpore, perform the following step
    ```
 2. Call the `export` API to export the `.ms` model file on the device.
    ```python
-   export(net, input_data, file_name="./lenet.ms", file_format='LITE')
+   export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
    ```
 
 Take the LeNet network as an example. The generated on-device model file is `lenet.ms`. The complete sample code `lenet.py` is as follows:
@@ -147,7 +159,7 @@ if __name__ == '__main__':
     if is_ckpt_exist:
         param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
         load_param_into_net(net, param_dict)
-        export(net, input_data, file_name="./lenet.ms", file_format='LITE')
+        export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
         print("export model success.")
     else:
         print("checkpoint file does not exist.")
@@ -159,7 +171,7 @@ Use the `.ms` model file and image data as input to create a session and impleme
 
 ![](./images/side_infer_process.png)
 
-Figure 1 On-device inference sequence diagram
+Figure 2 On-device inference sequence diagram
 1. Load the `.ms` model file to the memory buffer. The ReadFile function needs to be implemented by users, according to the [C++ tutorial](http://www.cplusplus.com/doc/tutorial/files/).
    ```cpp
    // read model file

tutorials/source_zh_cn/advanced_use/on_device_inference.md

@@ -15,7 +15,20 @@
 
 ## 概述
 
-MindSpore Predict是一个轻量级的深度神经网络推理引擎，提供了将MindSpore训练出的模型在端侧进行推理的功能。本教程介绍MindSpore Predict的编译方法和使用指南。
+MindSpore Lite是一个轻量级的深度神经网络推理引擎，提供了将MindSpore训练出的模型在端侧进行推理的功能。本教程介绍MindSpore Lite的编译方法和使用指南。
+
+![](./images/on_device_inference_frame.png)
+
+图1：端侧推理架构图
+
+MindSpore Lite的框架主要由Frontend、IR、Backend、Lite RT、Micro构成。
+
+- Frontend：用于模型的生成，用户可以使用模型构建接口构建模型，或者将第三方模型转化为MindSpore模型。
+- IR：包含MindSpore的Tensor定义、算子原型定义、图定义，后端优化基于IR进行。
+- Backend：包含图优化，量化等功能。图优化分为两部分：high-level优化与硬件无关，如算子融合、常量折叠等，low-level优化与硬件相关；量化，包括权重量化、激活值量化等多种训练后量化手段。
+- Lite RT：推理运行时，由session提供对外接口，kernel registry为算子注册器，scheduler为算子异构调度器，executor为算子执行器。Lite RT与Micro共享底层的算子库、内存分配、运行时线程池、并行原语等基础设施层。
+- Micro：Code-Gen根据模型生成.c文件，底层算子库等基础设施与Lite RT共用。
+
 
 ## 编译方法
 
@@ -91,7 +104,7 @@ MindSpore进行端侧模型推理的步骤如下。
    ```
 2. 调用`export`接口，导出端侧模型文件(`.ms`)。
    ```python
-   export(net, input_data, file_name="./lenet.ms", file_format='LITE')
+   export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
    ```
 
 以LeNet网络为例，生成的端侧模型文件为`lenet.ms`，完整示例代码`lenet.py`如下。
@@ -146,7 +159,7 @@ if __name__ == '__main__':
     if is_ckpt_exist:
         param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
         load_param_into_net(net, param_dict)
-        export(net, input_data, file_name="./lenet.ms", file_format='LITE')
+        export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
         print("export model success.")
     else:
         print("checkpoint file does not exist.")
@@ -158,7 +171,7 @@ if __name__ == '__main__':
 
 ![](./images/side_infer_process.png)
 
-图1：端侧推理时序图
+图2：端侧推理时序图
 1. 加载`.ms`模型文件到内存缓冲区，ReadFile函数功能需要用户参考[C++教程](http://www.cplusplus.com/doc/tutorial/files/)自行实现。
    ```cpp
    // read model file