Imporve Ascend docs

docs/cn/build_and_install/huawei_ascend.md

@@ -94,6 +94,11 @@ python setup.py bdist_wheel
 
 #编译完成后,请用户自行安装当前目录的dist文件夹内的whl包.
 ```
+## 五.昇腾部署时开启FlyCV
+[FlyCV](https://github.com/PaddlePaddle/FlyCV) 是一款高性能计算机图像处理库, 针对ARM架构做了很多优化, 相比其他图像处理库性能更为出色.
+FastDeploy现在已经集成FlyCV, 用户可以在支持的硬件平台上使用FlyCV, 实现模型端到端推理性能的加速.
+模型端到端推理中, 预处理和后处理阶段为CPU计算, 当用户使用ARM CPU + 昇腾的硬件平台时, 我们推荐用户使用FlyCV, 可以实现端到端的推理性能加速, 详见[FLyCV使用文档](./boost_cv_by_flycv.md)
+
 
 - 华为昇腾NPU 上使用C++部署 PaddleClas 分类模型请参考：[PaddleClas 华为升腾NPU C++ 部署示例](../../../examples/vision/classification/paddleclas/ascend/cpp/README.md)
 - 华为昇腾NPU 上使用Python部署 PaddleClas 分类模型请参考：[PaddleClas 华为升腾NPU Python 部署示例](../../../examples/vision/classification/paddleclas/ascend/python/README.md)

docs/cn/faq/use_sdk_on_ascend.md

@@ -19,11 +19,3 @@ source fastdeploy-ascend/fastdeploy_init.sh
 ```
 
 注意此命令执行后仅在当前的命令环境中生效（切换一个新的终端窗口，或关闭窗口重新打开后会无效），如若需要在系统中持续生效，可将这些环境变量加入到`~/.bashrc`文件中。
-
-# 昇腾部署时开启FlyCV
-[FlyCV](https://github.com/PaddlePaddle/FlyCV) 是一款高性能计算机图像处理库, 针对ARM架构做了很多优化, 相比其他图像处理库性能更为出色.
-FastDeploy现在已经集成FlyCV, 用户可以在支持的硬件平台上使用FlyCV, 实现模型端到端推理性能的加速.
-
-模型端到端推理中, 预处理和后处理阶段为CPU计算, 当用户使用ARM CPU + 昇腾的硬件平台时, 我们推荐用户使用FlyCV, 可以实现端到端的推理性能加速, 详见以下使用文档.
-
-- [FLyCV使用文档](./boost_cv_by_flycv.md)

docs/en/build_and_install/huawei_ascend.md

@@ -93,6 +93,13 @@ python setup.py bdist_wheel
 #After the compilation is complete, please install the whl package in the dist folder of the current directory.
 ```
 
+## Enable FlyCV for Ascend deployment
+
+[FlyCV](https://github.com/PaddlePaddle/FlyCV) is a high performance computer image processing library, providing better performance than other image processing libraries, especially in the ARM architecture.
+FastDeploy is now integrated with FlyCV, allowing users to use FlyCV on supported hardware platforms to accelerate model end-to-end inference performance.
+In end-to-end model inference, the pre-processing and post-processing phases are CPU computation, we recommend using FlyCV for end-to-end inference performance acceleration when you are using ARM CPU + Ascend hardware platform. See [Enable FlyCV](./boost_cv_by_flycv.md) documentation for details.
+
+
 Deploying PaddleClas Classification Model on Huawei Ascend NPU using C++ please refer to: [PaddleClas Huawei Ascend NPU C++ Deployment Example](../../../examples/vision/classification/paddleclas/ascend/cpp/README.md)
 
 Deploying PaddleClas classification model on Huawei Ascend NPU using Python please refer to: [PaddleClas Huawei Ascend NPU Python Deployment Example](../../../examples/vision/classification/paddleclas/ascend/python/README.md)

docs/en/faq/use_sdk_on_ascend.md

@@ -19,11 +19,3 @@ source fastdeploy-ascend/fastdeploy_init.sh
 ```
 
 Note that this command only takes effect in the current command environment after execution (switching to a new terminal window, or closing the window and reopening it will not work), if you need to keep it in effect on the system, add these environment variables to the `~/.bashrc` file.
-
-# Enable FlyCV for Ascend deployment
-
-[FlyCV](https://github.com/PaddlePaddle/FlyCV) is a high performance computer image processing library, providing better performance than other image processing libraries, especially in the ARM architecture.
-FastDeploy is now integrated with FlyCV, allowing users to use FlyCV on supported hardware platforms to accelerate model end-to-end inference performance.
-
-In end-to-end model inference, the pre-processing and post-processing phases are CPU computation, we recommend using FlyCV for end-to-end inference performance acceleration when you are using ARM CPU + Ascend hardware platform. See the following documentation for details.
-- [Enable FlyCV](./boost_cv_by_flycv.md)