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首页> 外文期刊>IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems >Toward Efficient Execution of Mainstream Deep Learning Frameworks on Mobile Devices: Architectural Implications
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Toward Efficient Execution of Mainstream Deep Learning Frameworks on Mobile Devices: Architectural Implications

机译：在移动设备上有效地执行主流深度学习框架：架构影响

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摘要

In recent years, continuous growing interests have been seen in bringing artificial intelligence capabilities to mobile devices. However, the related work still faces several issues, such as constrained computation and memory resources, power drain, and thermal limitation. To develop deep learning (DL) algorithms on mobile devices, we need to understand their behaviors. In this article, we explore the architectural behaviors of some mainstream DL frameworks on mobile devices by performing a comprehensive characterization of performance, accuracy, energy efficiency, and thermal behaviors. We experimentally choose four model compression methods to perform on networks and in addition, analyze the related impact on the nodes amount, memory, execution time, model size, inference time, energy consumption, and thermal distribution. With insights into DL-based mobile application characteristics, we hope to guide the design of future smartphone platforms for lower energy consumption.
机译：近年来，对移动设备的人工智能能力带来了不断增长的兴趣。然而，相关工作仍然面临着几个问题，例如约束的计算和内存资源，功率漏极和热限制。为了在移动设备上开发深度学习（DL）算法，我们需要了解他们的行为。在本文中，我们通过执行性能，准确性，能效和热行为的全面表征，探讨移动设备上一些主流DL框架的架构行为。我们通过实验选择四种模型压缩方法来执行网络，然后分析对节点金额，存储器，执行时间，模型大小，推理时间，能量消耗和热分布的相关影响。凭借洞察DL的移动应用特征，我们希望指导未来智能手机平台的设计，以降低能耗。

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