摘要:利用DeepX加速嵌入式深度学习

N. Lane, S. Bhattacharya, Petko Georgiev, Claudio Forlivesi, F. Kawsar
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引用次数: 1

摘要

深度学习彻底改变了传感器测量的解释方式,深度学习的应用在许多领域的推理精度上都有了很大的飞跃。然而,对内存和计算能力的巨大需求阻碍了这些新型计算技术在资源受限的可穿戴和移动平台上的广泛采用。在这个演示中,我们展示了DeepX,一个软件加速器,用于在资源受限的嵌入式平台上有效运行深度神经网络和卷积神经网络,例如Nvidia Tegra K1和Qualcomm Snapdragon 400。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demonstration Abstract: Accelerating Embedded Deep Learning Using DeepX
Deep learning has revolutionized the way sensor measurements are interpreted and application of deep learning has seen a great leap in inference accuracies in a number of fields. However, the significant requirement for memory and computational power has hindered the wide scale adoption of these novel computational techniques on resource constrained wearable and mobile platforms. In this demonstration we present DeepX, a software accelerator for efficiently running deep neural networks and convolutional neural networks on resource constrained embedded platforms, e.g., Nvidia Tegra K1 and Qualcomm Snapdragon 400.
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