From computer vision and speech recognition to forecasting trajectories in autonomous vehicles, deep learning approaches are at the forefront of so many domains. Deep learning models are developed using plethora of high-level, generic frameworks and libraries. Running those models on the mobile devices require hardware-aware optimizations and in most cases converting the models to other formats or using a third-party framework. In reality, most of the developed models need to undergo a process of conversion, adaptation, and, in some cases, full retraining to match the requirements and features of the framework that is deploying the model on the target platform. Variety of hardware platforms with heterogeneous computing elements, from wearable devices to high-performance GPU clusters are used to run deep learning models. In this paper, we present the existing challenges, obstacles, and practical solutions towards deploying deep learning models on mobile devices.

中文翻译：

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在移动设备上部署深度学习模型的挑战和障碍

从计算机视觉和语音识别到自动驾驶汽车的预测轨迹，深度学习方法处于众多领域的最前沿。深度学习模型是使用大量高级通用框架和库开发的。在移动设备上运行这些模型需要对硬件进行优化，并且在大多数情况下，需要将模型转换为其他格式或使用第三方框架。实际上，大多数已开发的模型都需要进行转换，调整以及在某些情况下的完全重新培训，以匹配将模型部署在目标平台上的框架的要求和功能。从可穿戴设备到高性能GPU群集，各种具有异构计算元素的硬件平台都用于运行深度学习模型。在本文中，