Imporve Ascend docs

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## 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)
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)
## 六.昇腾部署Demo参考
- 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/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/python/README.md)

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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)
## Deployment demo reference
- Deploying PaddleClas Classification Model on Huawei Ascend NPU using C++ please refer to: [PaddleClas Huawei Ascend NPU C++ Deployment Example](../../../examples/vision/classification/paddleclas/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)
- Deploying PaddleClas classification model on Huawei Ascend NPU using Python please refer to: [PaddleClas Huawei Ascend NPU Python Deployment Example](../../../examples/vision/classification/paddleclas/python/README.md)