NVIDIA Boosts IQ of Self-Driving Cars With World's First In-Car Artificial Intelligence Supercomputer

LAS VEGAS, NV--(Marketwired - Jan 4, 2016) - CES -- Accelerating the race to autonomous cars, NVIDIA (NASDAQ: NVDA) today launched NVIDIA DRIVE™ PX 2 -- the world's most powerful engine for in-vehicle artificial intelligence.

NVIDIA DRIVE PX 2 allows the automotive industry to use artificial intelligence to tackle the complexities inherent in autonomous driving. It utilizes deep learning on NVIDIA's most advanced GPUs for 360-degree situational awareness around the car, to determine precisely where the car is and to compute a safe, comfortable trajectory.

"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."

24 Trillion Deep Learning Operations per Second
Created to address the needs of NVIDIA's automotive partners for an open development platform, DRIVE PX 2 provides unprecedented amounts of processing power for deep learning, equivalent to that of 100 MacBook Pros.

Its two next-generation Tegra^® processors plus two next-generation discrete GPUs, based on the Pascal™ architecture, deliver up to 24 trillion deep learning operations per second, which are specialized instructions that accelerate the math used in deep learning network inference. That's over 10 times more computational horsepower than the previous-generation product.

DRIVE PX 2's deep learning capabilities enable it to quickly learn how to address the challenges of everyday driving, such as unexpected road debris, erratic drivers and construction zones. Deep learning also addresses numerous problem areas where traditional computer vision techniques are insufficient -- such as poor weather conditions like rain, snow and fog, and difficult lighting conditions like sunrise, sunset and extreme darkness.