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2017 GTC San Jose

S7210 - Deep Learning Applications for Embedded Avionics on the Jetson Platform

Session Speakers
Session Description

We'll discuss the uses and tradeoffs of semantic segmentation and detection networks when deployed on the Jetson TX1. There is significant research into deep learning semantic segmentation and detection networks since these can both detect and localize numerous objects within the image. We use FCN (fcn.berkeleyvision.org) as an example of a semantic segmentation network, and the DIGITS DetectNet as an example of a detection network. These networks require significant computing resources for inferencing, and within embedded avionics applications we wish to provide the best tradeoff of performance-per-watt by leveraging these networks on the Jetson TX1. We'll explore characteristics of these deep learning networks, how these deep learning capabilities can be utilized on the Jetson TX1 platform, and characterize their runtime performance on the Jetson TX1 compared to larger GPU systems.


Additional Session Information
Intermediate
Talk
Computer Vision and Machine Vision Deep Learning and AI Federal Intelligent Machines and IoT
Aerospace Defense
25 minutes
Session Schedule