提交 e8a38b99 编写于 作者: H huzhiqiang 提交者: GitHub

change the url_source of readme img into github network (#2614)

上级 580502cc
...@@ -44,7 +44,7 @@ Framework compatibility: In addition to models trained on PaddlePaddle, those tr ...@@ -44,7 +44,7 @@ Framework compatibility: In addition to models trained on PaddlePaddle, those tr
Paddle Lite is designed to support a wide range of hardwares and devices, and it enables mixed execution of a single model on multiple devices, optimization on various phases, and leight-weighted applications on devices. Paddle Lite is designed to support a wide range of hardwares and devices, and it enables mixed execution of a single model on multiple devices, optimization on various phases, and leight-weighted applications on devices.
![img](https://github.com/Superjomn/_tmp_images/raw/master/images/paddle-lite-architecture.png) ![img](https://user-images.githubusercontent.com/45189361/70908123-6ce4fd00-2045-11ea-97e1-ad08446c5c86.png)
As is shown in the figure above, analysis phase includes Machine IR module, and it enables optimizations like Op fusion and redundant computation pruning. Besides, excecution phase only involves Kernal exevution, so it can be deployed on its own to ensure maximized light-weighted deployment. As is shown in the figure above, analysis phase includes Machine IR module, and it enables optimizations like Op fusion and redundant computation pruning. Besides, excecution phase only involves Kernal exevution, so it can be deployed on its own to ensure maximized light-weighted deployment.
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...@@ -34,7 +34,7 @@ Paddle Lite为Paddle-Mobile的升级版,定位支持包括手机移动端在 ...@@ -34,7 +34,7 @@ Paddle Lite为Paddle-Mobile的升级版,定位支持包括手机移动端在
PaddleLite 的架构设计着重考虑了对多硬件和平台的支持,并且强化了多个硬件在一个模型中混合执行的能力,多个层面的性能优化处理,以及对端侧应用的轻量化设计。 PaddleLite 的架构设计着重考虑了对多硬件和平台的支持,并且强化了多个硬件在一个模型中混合执行的能力,多个层面的性能优化处理,以及对端侧应用的轻量化设计。
![](https://github.com/Superjomn/_tmp_images/raw/master/images/paddle-lite-architecture.png) ![](https://user-images.githubusercontent.com/45189361/70908123-6ce4fd00-2045-11ea-97e1-ad08446c5c86.png)
其中,Analysis Phase 包括了 MIR(Machine IR) 相关模块,能够对原有的模型的计算图针对具体的硬件列表进行算子融合、计算裁剪 在内的多种优化。Execution Phase 只涉及到Kernel 的执行,且可以单独部署,以支持极致的轻量级部署。 其中,Analysis Phase 包括了 MIR(Machine IR) 相关模块,能够对原有的模型的计算图针对具体的硬件列表进行算子融合、计算裁剪 在内的多种优化。Execution Phase 只涉及到Kernel 的执行,且可以单独部署,以支持极致的轻量级部署。
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