NVIDIA Drive PX 2 is extremely powerful AI driven super computer for your car
This year, its all going to be about making automobiles smarter and we have seen in the past that Apple and Google are working round the clock to get an upper hand and head start in this area. Today, at CES 2016, Nvidia has announced their entry into the arena. Drive PX 2 is the first in-car artificial intelligence powered deep learning lunch box sized mobile super computer. Drive PX 2 can embed up to 12 CPU cores and has Pascal GPU built in. As a result, the Drive PX 2 can hit 6 Teraflops per second speed.
“Drivers deal with an infinitely complex world,” said Jen-Hsun Huang, co-founder and CEO, NVIDIA. “Modern artificial intelligence and GPU breakthroughs enable us to finally tackle the daunting challenges of self-driving cars.
“NVIDIA’s GPU is central to advances in deep learning and supercomputing. We are leveraging these to create the brain of future autonomous vehicles that will be continuously alert, and eventually achieve superhuman levels of situational awareness. Autonomous cars will bring increased safety, new convenient mobility services and even beautiful urban designs — providing a powerful force for a better future.”
The Drive PX 2 is built on 16nm FinFET fabrication process and has 24 deep learning Tops of power. The unit draws 250W of power and is liquid cooled. Thanks to 24 trillion operations per second, the Drive PX 2 is as powerful as 150 MacBook Pro workstations. The GPU on the other hand can reach up to 8 trillion operations per second. It can learn how to address how to handle unexpected road debris, erratic driving behavior etc and also monitors weather conditions to find out any upcoming poor weather conditions.
NVIDIA DriveWorks is a suite of software tools, libraries and modules, created to accelerate development and testing of autonomous vehicles. Devs can perform sensor calibration, get surround data, synchronization recording and processing of data from sensors.
“Using NVIDIA’s DIGITS deep learning platform, in less than four hours we achieved over 96 percent accuracy using Ruhr University Bochum’s traffic sign database. While others invested years of development to achieve similar levels of perception with classical computer vision algorithms, we have been able to do it at the speed of light.”