TensorFlow机器学习针对骁龙835和Hexagon 682 DSP进行优化


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针对Qualcomm Hexagon DSP进行优化的应用程序能够更快地运行,耗电更少。Qualcomm目前正为Google的机器学习架构TensorFlow提供更多支持。目前TensorFlow针对Hexagon 682 DSP进行了专门优化,而这项技术集成于我们的顶级处理器骁龙835之中。

TensorFlow是Google的机器学习软件库,并且是开源的,开发者可以免费使用TensorFlow来创建模型,并在其手机和电脑应用程序或云服务中加入人工智能。开发者可采用多种方式充分利用骁龙835的异构核心,其中最佳方式是充分利用DSP。Google已在多款产品中使用TensorFlow机器学习功能,包括Google Photos和Google Cloud Speech。


用于GoogleNet Inception DNN(深度神经网络)的最佳功率和性能。选择核心来匹配用户体验。(实际性能可能有所差别)
用于GoogleNet Inception DNN(深度神经网络)的最佳功率和性能。选择核心来匹配用户体验。(实际性能可能有所差别)

Qualcomm Technologies与Google密切合作之后,TensorFlow将运行在DSP上,让应用程序运行得更快、更高效,而无需使整个系统都工作。

Qualcomm Technologies还通过Qualcomm 骁龙神经处理引擎SDK为Tensorflow提供支持。这是一种异构软件框架,让OEM厂商能够在CPU、GPU或DSP等核心上运行Tensorflow模型,为预期的用户体验提供最佳性能和功率分布。

我们做了一个测试,Google创建了一个内置TensorFlow的应用程序,并将其加载到两部完全相同的智能手机上。应用程序能够识别放在智能手机相机面前的实物。唯一的区别是,一部智能手机在CPU上运行应用程序,而另一个在Hexagon DSP上运行。






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