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2017 GTC San Jose

S7571 - High-Performance Deep Learning on Embedded Devices MXNet

Session Speakers
Session Description

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.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Intelligent Machines and IoT Performance Optimization
Hardware / Semiconductor Software
50 minutes
Session Schedule