!833 update the English version of architecture.md

Merge pull request !833 from mengchunyang/master

lite/docs/source_en/architecture.md

-# Overall Architecture
+# Overall Architecture
 
-<a href="https://gitee.com/mindspore/docs/blob/master/lite/docs/source_en/architecture.md" target="_blank"><img src="../_static/logo_source.png"></a>
+<a href="https://gitee.com/mindspore/docs/blob/master/lite/docs/source_en/architecture.md" target="_blank"><img src="./_static/logo_source.png"></a>
+
+The overall architecture of MindSpore Lite is as follows:
+
+![architecture](./images/MindSpore-Lite-architecture.png)
+
+- **Frontend:** generates models. You can use the model building API to build models and convert third-party models and models trained by MindSpore to MindSpore Lite models. Third-party models include TensorFlow Lite, Caffe 1.0, and ONNX models.
+
+- **IR:** defines the tensors, operators, and graphs of MindSpore.
+
+- **Backend:** optimizes graphs based on IR, including graph high level optimization (GHLO), graph low level optimization (GLLO), and quantization. GHLO is responsible for hardware-independent optimization, such as operator fusion and constant folding. GLLO is responsible for hardware-related optimization. Quantizer supports quantization methods after training, such as weight quantization and activation value quantization.
+
+- **Runtime:** inference runtime of intelligent devices. Sessions are responsible for session management and provide external APIs. The thread pool and parallel primitives are responsible for managing the thread pool used for graph execution. Memory allocation is responsible for memory overcommitment of each operator during graph execution. The operator library provides the CPU and GPU operators. 
+
+- **Micro:** runtime of IoT devices, including the model generation .c file, thread pool, memory overcommitment, and operator library.
+
+Runtime and Micro share the underlying infrastructure layers, such as the operator library, memory allocation, thread pool, and parallel primitives. 

lite/tutorials/source_en/use/benchmark_tool.md

@@ -68,13 +68,13 @@ When using the Benchmark tool to perform benchmark testing on different MindSpor
 The main test indicator of the performance test performed by the Benchmark tool is the duration of a single forward inference. In a performance test, you do not need to set benchmark data parameters such as `calibDataPath`. For example:
 
 ```bash
-./benchmark --modelPath=./models/face_age.ms
+./benchmark --modelPath=./models/test_benchmark.ms
 ```
 
 This command uses a random input, and other parameters use default values. After this command is executed, the following statistics are displayed. The statistics include the minimum duration, maximum duration, and average duration of a single inference after the tested model runs for the specified number of inference rounds.
 
 ```
-Model = face_age.ms, numThreads = 2, MinRunTime = 72.228996 ms, MaxRuntime = 73.094002 ms, AvgRunTime = 72.556000 ms
+Model = test_benchmark.ms, numThreads = 2, MinRunTime = 72.228996 ms, MaxRuntime = 73.094002 ms, AvgRunTime = 72.556000 ms
 ```
 
 ### Accuracy Test
@@ -82,7 +82,7 @@ Model = face_age.ms, numThreads = 2, MinRunTime = 72.228996 ms, MaxRuntime = 73.
 The accuracy test performed by the Benchmark tool is to verify the accuracy of the MinSpore model output by setting benchmark data. In an accuracy test, in addition to the `modelPath` parameter, the `calibDataPath` parameter must be set. For example:
 
 ```bash
-./benchmark --modelPath=./models/face_age.ms --inDataPath=./input/face_age.bin --device=CPU --accuracyThreshold=3 --calibDataPath=./output/face_age.out
+./benchmark --modelPath=./models/test_benchmark.ms --inDataPath=./input/test_benchmark.bin --device=CPU --accuracyThreshold=3 --calibDataPath=./output/test_benchmark.out
 ```
 
 This command specifies the input data and benchmark data of the tested model, specifies that the model inference program runs on the CPU, and sets the accuracy threshold to 3%. After this command is executed, the following statistics are displayed, including the single input data of the tested model, output result and average deviation rate of the output node, and average deviation rate of all nodes.

lite/tutorials/source_en/use/timeprofiler_tool.md

@@ -46,10 +46,10 @@ The following describes the parameters in detail.
 
 ## Example
 
-Take the `tcpclassify.ms` model as an example and set the number of model inference cycles to 10. The command for using TimeProfiler to analyze the time consumption at the network layer is as follows:
+Take the `test_timeprofiler.ms` model as an example and set the number of model inference cycles to 10. The command for using TimeProfiler to analyze the time consumption at the network layer is as follows:
 
 ```bash
-./timeprofiler --modelPath=./models/tcpclassify.ms --loopCount=10
+./timeprofiler --modelPath=./models/test_timeprofiler.ms --loopCount=10
 ```
 
 After this command is executed, the TimeProfiler tool outputs the statistics on the running time of the model at the network layer. In this example, the command output is as follows: The statistics are displayed by`opName` and `optype`. `opName` indicates the operator name, `optype` indicates the operator type, and `avg` indicates the average running time of the operator per single run, `percent` indicates the ratio of the operator running time to the total operator running time, `calledTimess` indicates the number of times that the operator is run, and `opTotalTime` indicates the total time that the operator is run for a specified number of times. Finally, `total time` and `kernel cost` show the average time consumed by a single inference operation of the model and the sum of the average time consumed by all operators in the model inference, respectively.

lite/tutorials/source_zh_cn/use/benchmark_tool.md

@@ -68,13 +68,13 @@ Benchmark工具是一款可以对MindSpore Lite模型进行基准测试的工具
 Benchmark工具进行的性能测试主要的测试指标为模型单次前向推理的耗时。在性能测试任务中，不需要设置`calibDataPath`等标杆数据参数。例如：
 
 ```bash
-./benchmark --modelPath=./models/face_age.ms
+./benchmark --modelPath=./models/test_benchmark.ms
 ```
 
 这条命令使用随机输入，其他参数使用默认值。该命令执行后会输出如下统计信息，该信息显示了测试模型在运行指定推理轮数后所统计出的单次推理最短耗时、单次推理最长耗时和平均推理耗时。
 
 ```
-Model = face_age.ms, numThreads = 2, MinRunTime = 72.228996 ms, MaxRuntime = 73.094002 ms, AvgRunTime = 72.556000 ms
+Model = test_benchmark.ms, numThreads = 2, MinRunTime = 72.228996 ms, MaxRuntime = 73.094002 ms, AvgRunTime = 72.556000 ms
 ```
 
 ### 精度测试
@@ -82,7 +82,7 @@ Model = face_age.ms, numThreads = 2, MinRunTime = 72.228996 ms, MaxRuntime = 73.
 Benchmark工具进行的精度测试主要是通过设置标杆数据来对比验证MindSpore Lite模型输出的精确性。在精确度测试任务中，除了需要设置`modelPath`参数以外，还必须设置`calibDataPath`参数。例如：
 
 ```bash
-./benchmark --modelPath=./models/face_age.ms --inDataPath=./input/face_age.bin --device=CPU --accuracyThreshold=3 --calibDataPath=./output/face_age.out
+./benchmark --modelPath=./models/test_benchmark.ms --inDataPath=./input/test_benchmark.bin --device=CPU --accuracyThreshold=3 --calibDataPath=./output/test_benchmark.out
 ```
 
 这条命令指定了测试模型的输入数据、标杆数据，同时指定了模型推理程序在CPU上运行，并指定了准确度阈值为3%。该命令执行后会输出如下统计信息，该信息显示了测试模型的单条输入数据、输出节点的输出结果和平均偏差率以及所有节点的平均偏差率。

lite/tutorials/source_zh_cn/use/timeprofiler_tool.md

@@ -46,10 +46,10 @@ TimeProfiler工具可以对MindSpore Lite模型网络层的前向推理进行耗
 
 ## 使用示例
 
-使用TimeProfiler对`tcpclassify.ms`模型的网络层进行耗时分析，并且设置模型推理循环运行次数为10，则其命令代码如下：
+使用TimeProfiler对`test_timeprofiler.ms`模型的网络层进行耗时分析，并且设置模型推理循环运行次数为10，则其命令代码如下：
 
 ```bash
-./timeprofiler --modelPath=./models/tcpclassify.ms --loopCount=10
+./timeprofiler --modelPath=./models/test_timeprofiler.ms --loopCount=10
 ```
 
 该条命令执行后，TimeProfiler工具会输出模型网络层运行耗时的相关统计信息。对于本例命令，输出的统计信息如下。其中统计信息按照`opName`和`optype`两种划分方式分别显示，`opName`表示算子名，`optype`表示算子类别，`avg`表示该算子的平均单次运行时间，`percent`表示该算子运行耗时占所有算子运行总耗时的比例，`calledTimess`表示该算子的运行次数，`opTotalTime`表示该算子运行指定次数的总耗时。最后，`total time`和`kernel cost`分别显示了该模型单次推理的平均耗时和模型推理中所有算子的平均耗时之和。