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MobiSys ... : the ... International Conference on Mobile Systems, Applications and Services. International Conference on Mobile Systems, Applications, and Services最新文献

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RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices. RSTensorFlow: GPU支持的TensorFlow用于商用Android设备上的深度学习。
Moustafa Alzantot, Yingnan Wang, Zhengshuang Ren, Mani B Srivastava

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support.

移动设备已经成为我们日常生活中必不可少的一部分。由于移动设备的计算能力不断增强,加上最近在人工智能方面取得的进展,它们已经演变成在许多任务中扮演智能助手的角色,而不仅仅是打电话的工具。然而,流行和常用的机器智能工具和框架仍然缺乏正确利用移动设备上可用的异构计算资源的能力。在本文中,我们研究了在运行深度学习模型时利用商用android设备上可用的异构(CPU和GPU)计算资源的好处。我们利用异构计算框架RenderScript来加速深度学习模型在商用Android设备上的执行。我们的系统是作为流行的开源框架TensorFlow的扩展实现的。通过将我们的加速框架紧密集成到TensorFlow中,机器学习工程师现在可以轻松地利用移动设备上的异构计算资源,而不需要任何额外的工具。我们在不同的android手机模型上评估了我们的系统,以研究在GPU上运行不同神经网络操作的权衡。我们还比较了仅在CPU上运行不同模型架构(如卷积和循环神经网络)与使用异构计算资源的性能。我们的结果表明,尽管手机上的gpu能够在移动设备上的矩阵乘法中提供实质性的性能提升。因此,涉及到大矩阵乘法的模型可以运行得更快(大约为1 / 3)。在我们的实验中快了3倍)由于GPU的支持。
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引用次数: 46
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MobiSys ... : the ... International Conference on Mobile Systems, Applications and Services. International Conference on Mobile Systems, Applications, and Services
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