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GTC ON-DEMAND

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Abstract:

Learn how to compile and run an optimized version of the MXNet deep learning framework for various embedded (IoT) devices, as well as see the wide range of exciting applications that running deep-network inference in near-realtime on "edge" devices opens up. Specifically, we'll be showing performance numbers for a variety of deep learning models based in MXNet running on Raspberry Pis as well as TK1 processors, demonstrating the massive efficiency gains on embedded devices MXNet yields over comparable frameworks. We'll then demo the power of real-time image processing via deep learning models with an example application walkthrough. Finally, we'll demonstrate how to use AWS IoT services to massively augment the flexibility and reliability of the models running in our example application.

Learn how to compile and run an optimized version of the MXNet deep learning framework for various embedded (IoT) devices, as well as see the wide range of exciting applications that running deep-network inference in near-realtime on "edge" devices opens up. Specifically, we'll be showing performance numbers for a variety of deep learning models based in MXNet running on Raspberry Pis as well as TK1 processors, demonstrating the massive efficiency gains on embedded devices MXNet yields over comparable frameworks. We'll then demo the power of real-time image processing via deep learning models with an example application walkthrough. Finally, we'll demonstrate how to use AWS IoT services to massively augment the flexibility and reliability of the models running in our example application.

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Topics:
Intelligent Machines, IoT & Robotics, Artificial Intelligence and Deep Learning, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7571
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