Update doc (#7247)

* [Doc] add docs for features introduction (#7194)

* [DOCS] refine java api docs (#7229)

* [DOCS] add development guides docs for users (#7221)

* [doc] add paddle lite introduction (#7219)

* test=document_fix

* test=document_fix

Author: DannyIsFunny

README.md

@@ -54,20 +54,9 @@ Paddle Lite提供了C++、Java、Python三种API，并且提供了相应API的
 
 ## 主要特性
 
-- **多硬件支持：**
-	- Paddle Lite架构已经验证和完整支持从 Mobile 到 Server [多种硬件平台](https://paddle-lite.readthedocs.io/zh/latest/introduction/support_hardware.html)，包括 ARM CPU、Mali GPU、Adreno GPU、英伟达 GPU、苹果 GPU、华为 NPU，以及 FPGA 等，且正在不断增加更多新硬件支持。
-	- 各个硬件平台的 Kernel 在代码层和执行层互不干扰，用户不仅可以自由插拔任何硬件，还支持任意系统可见硬件之间的[混合调度](https://paddle-lite.readthedocs.io/zh/latest/introduction/tech_highlights.html#id7)。
-- **轻量级部署**：
-	- Paddle Lite在设计上对图优化模块和执行引擎实现了良好的解耦拆分，移动端可以直接部署执行阶段，无任何第三方依赖。
-	- 包含完整的80个 op+85个 Kernel 的动态库，对于ARMV7只有800K，ARMV8下为1.3M，并可以通过[裁剪预测](https://paddle-lite.readthedocs.io/zh/latest/source_compile/library_tailoring.html)库进一步减小预测库文件大小。
-- **高性能：**
-	- 极致的 ARM CPU 性能优化：针对不同微架构特点实现kernel的定制，最大发挥计算性能，在主流模型上展现出领先的速度优势。
-	- 支持 [PaddleSlim模型压缩工具](https://github.com/PaddlePaddle/PaddleSlim)：支持量化训练、离线量化等多种量化方式，最优可在不损失精度的前提下进一步提升模型推理性能。性能数据请参考 [benchmark](https://paddlepaddle.github.io/Paddle-Lite/develop/benchmark/)。
-- **多模型多算子**：
-	- Paddle Lite和PaddlePaddle训练框架的OP对齐，提供广泛的模型支持能力。
-	- 目前已严格验证24个模型200个OP的精度和性能，对视觉类模型做到了较为充分的支持，覆盖分类、检测和定位，包含了特色的OCR模型的支持，并在不断丰富中。具体请参考[支持OP](https://paddle-lite.readthedocs.io/zh/latest/introduction/support_operation_list.html)。
-- **强大的图分析和优化能力**：
-	- 不同于常规的移动端预测引擎基于 Python 脚本工具转化模型， Lite 架构上有完整基于 C++ 开发的 IR 及相应 Pass 集合，以支持操作融合，计算剪枝，存储优化，量化计算等多类计算图优化。更多的优化策略可以简单通过 [新增 Pass](https://paddle-lite.readthedocs.io/zh/latest/develop_guides/add_new_pass.html) 的方式模块化支持。
+- 支持多平台：涵盖 Android、iOS、嵌入式 Linux 设备、Windows、macOS 和 Linux 主机
+- 支持多种语言：包括 Java、Python、C++
+- 轻量化和高性能：针对移动端设备的机器学习进行优化，压缩模型和二进制文件体积，高效推理，降低内存消耗
 
 ## 持续集成
 

README_en.md

@@ -11,31 +11,10 @@ For tutorials, please see [PaddleLite Document](https://paddle-lite.readthedocs.
 
 ## Key Features
 
-### Light Weight
+- Multiple platform support, covering Android and iOS devices, embedded Linux, windows, macOS and Linux computer.
+- Diverse language support, which includes Java, C++, and Python.
+- High performance and light weight: optimized for on-device machine learning, reduced model and binary size, efficient inference and reduced memory usage.
 
-On mobile devices, execution module can be deployed without third-party libraries, because our excecution module and analysis module are decoupled.
-
-On ARM V7, only 800KB are taken up, while on ARM V8, 1.3MB are taken up with the 80 operators and 85 kernels in the dynamic libraries provided by Paddle Lite.
-
-Paddle Lite enables immediate inference without extra optimization.
-
-### High Performance
-
-Paddle Lite enables device-optimized kernels, maximizing ARM CPU performance.
-
-It also supports INT8 quantizations with [PaddleSlim model compression tools](https://github.com/PaddlePaddle/models/tree/v1.5/PaddleSlim), reducing the size of models and increasing the performance of models.
-
-On Huawei NPU and FPGA, the performance is also boosted.
-
-The latest benchmark is located at [benchmark](https://paddlepaddle.github.io/Paddle-Lite/develop/benchmark/)
-
-### High Compatibility
-
-Hardware compatibility: Paddle Lite supports a diversity of hardwares — ARM CPU, Mali GPU, Adreno GPU, Nvidia GPU, Apple GPU, Huawei NPU and FPGA. In the near future, we will also support AI microchips from Cambricon and Bitmain.
-
-Model compatibility: The Op of Paddle Lite is fully compatible to that of PaddlePaddle. The accuracy and performance of 18 models (mostly CV models and OCR models) and 85 operators have been validated. In the future, we will also support other models.
-
-Framework compatibility: In addition to models trained on PaddlePaddle, those trained on Caffe and TensorFlow can also be converted to be used on Paddle Lite, via [X2Paddle](https://github.com/PaddlePaddle/X2Paddle). In the future to come, we will also support models of ONNX format.
 
 ## Architecture