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.