AI at the Edge: NVIDIA Jetson TX2 Small Supercomputer for UAVs and Robots

AI at the Edge: NVIDIA Jetson T2 Small Supercomputer UAVs and Robots
May 22, 2017 | Source: Hackaday, hackaday.com, 15 March 2017, Brian Benchoff

Last week, Nvidia announced the Jetson TX2, a high-performance single board computer designed to be the brains of self-driving cars, selfie-snapping drones, Alexa-like bots for the privacy-minded, and other applications that require a lot of processing on a significant power budget.

The TX2 is a tiny board bolted to a credit-card sized heat sink. For anyone who is already using the Jetson TX1, the TX2 will be a drop-in replacement. Compared to the Jetson TX1, the TX2 boasts twice as much RAM with more bandwidth, twice as much eMMC Flash, and can encode 2k video twice as fast. The CPU is a dual-core Nvidia Denver 2.0 and a quad-core ARM Cortex A57.

The Jetson TX2 has two power modes. The ‘Max Q’ setting is maximum energy efficiency, which when measuring with a meter, comes in at about 7.5 Watts. The ‘Max P’ setting is for maximum performance and comes in at around 15 Watts. In Max P mode, the performance is reportedly double that of the Jetson TX1.

What’s the takeaway on this? In synthetic benchmarks testing the CPU, the Nvidia Jetson TX2 is about four times as fast as the Raspberry Pi 3. It’s fast as hell... With VisionWorks, the Jetson was able to identify features relevant to driving across the golden gate bridge. It was able to use parallax to build a point cloud of a parking lot. The Jetson TX2 was stabilizing video in real time. A laptop could do this, but a Pi couldn’t.

But not all Deep Learning is playing with a camera; in the benchmarks released by Nvidia, the TX2 is almost twice as fast as the TX1 at GoogleNet inference performance. For AlexNet inference performance, The TX2 performs better and uses less power...This is not a toy. This is an engineering tool. This is a module that will power a self-driving car, or a selfie-capturing quadcopter. These are hard engineering problems that demand fast processing with a low power budget.