Update README_en.md (#2840)

* Update README_en.md

update the english doc in order to be consistent with #2839

* Update README_en.md

Author: onecatcn

README_en.md

@@ -279,7 +279,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
     </tr>
 </table>
 
-> ❗Note: The above capabilities are implemented based on GPU/CPU. PaddleX can also perform local inference and custom development on mainstream hardware such as Kunlunxin, Ascend, Cambricon, and Haiguang. The table below details the support status of the pipelines. For specific supported model lists, please refer to the [Model List (Kunlunxin XPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_xpu.html)/[Model List (Ascend NPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_npu.html)/[Model List (Cambricon MLU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_mlu.html)/[Model List (Haiguang DCU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_dcu.html). We are continuously adapting more models and promoting the implementation of high-performance and service-oriented deployment on mainstream hardware.
+> ❗Note: The above capabilities are implemented based on GPU/CPU. PaddleX can also perform local inference and custom development on mainstream hardware such as Kunlunxin, Ascend, Cambricon, and Hygon. The table below details the support status of the pipelines. For specific supported model lists, please refer to the [Model List (Kunlunxin XPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_xpu.html)/[Model List (Ascend NPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_npu.html)/[Model List (Cambricon MLU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_mlu.html)/[Model List (Hygon DCU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_dcu.html). We are continuously adapting more models and promoting the implementation of high-performance and service-oriented deployment on mainstream hardware.
 
 🔥🔥 **Support for Domestic Hardware Capabilities**
 
@@ -289,7 +289,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
     <th>Ascend 910B</th>
     <th>Kunlunxin R200/R300</th>
     <th>Cambricon MLU370X8</th>
-    <th>Haiguang Z100</th>
+    <th>Hygon Z100/K100AI</th>
   </tr>
   <tr>
     <td>OCR</td>