!5546 update README

Merge pull request !5546 from mengchunyang/r0.7

RELEASE.md

@@ -56,7 +56,6 @@
     * Add 93 TFLite op.
     * Add 24 Caffe op.
     * Add 62 ONNX op.
-    * Add support for windows.
     * Add 11 optimized passes, include fusion/const fold.
     * Support aware-training and Post-training quantization.
 * CPU

mindspore/lite/README.md

@@ -54,3 +54,14 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
    Load the model and perform inference. [Inference](https://www.mindspore.cn/lite/tutorial/en/master/use/runtime.html) is the process of running input data through the model to get output.
 
    MindSpore provides a series of pre-trained models that can be deployed on mobile device [example](#TODO).
+   
+## MindSpore Lite benchmark test result
+Base on MindSpore r0.7, we test a couple of networks on HUAWEI Mate30 (Hisilicon Kirin990) mobile phone, and get the test results below for your reference.
+   
+   | NetWork             | Thread Number | Average Run Time(ms) |
+   | ------------------- | ------------- | -------------------- |
+   | basic_squeezenet    | 4             | 9.10                 |
+   | inception_v3        | 4             | 69.361               |
+   | mobilenet_v1_10_224 | 4             | 7.137                |
+   | mobilenet_v2_10_224 | 4             | 5.569                |
+   | resnet_v2_50        | 4             | 48.691               |

mindspore/lite/README_CN.md

@@ -64,3 +64,14 @@ MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推
    主要完成模型推理工作，即加载模型，完成模型相关的所有计算。[推理](https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/runtime.html)是通过模型运行输入数据，获取预测的过程。
 
    MindSpore提供了一系列预训练模型部署在智能终端的[样例](#TODO)。
+   
+## MindSpore Lite性能参考数据
+我们在HUAWEI Mate30（Hisilicon Kirin990）手机上，基于MindSpore r0.7，测试了一组端侧常见网络的性能数据，供您参考：
+   
+   | 网络                | 线程数 | 平均推理时间(毫秒) |
+   | ------------------- | ------ | ------------------ |
+   | basic_squeezenet    | 4      | 9.10               |
+   | inception_v3        | 4      | 69.361             |
+   | mobilenet_v1_10_224 | 4      | 7.137              |
+   | mobilenet_v2_10_224 | 4      | 5.569              |
+   | resnet_v2_50        | 4      | 48.691             |