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Posts Tagged ‘GPU’

NVIDIA’s AI Computer Drives AVs

Thursday, October 19th, 2017

This week NVIDIA unveiled what it claims to be the world’s first artificial intelligence computer designed specifically to “drive” fully autonomous vehicles.

The new system, codenamed Pegasus, brings the NVIDIA® DRIVE™ PX AI computing platform for handling Level 5 driverless vehicles (Level 5 is ”steering wheel optional.” In other words, no human intervention is required, for example, a robotic taxi). NVIDIA DRIVE PX Pegasus can perform over 320 trillion operations per second — more than 10x the performance of its predecessor, NVIDIA DRIVE PX 2.

NVIDIA DRIVE PX Pegasus is intended to help make a new class of vehicles possible that can operate without a driver — fully autonomous vehicles without steering wheels, pedals, or mirrors, and interiors that feel more like a living room or office than a vehicle. They will arrive on demand to safely take passengers to their destinations, bringing mobility to everyone, including the elderly and disabled.

One of the driving forces behind autonomous vehicles is to recapture millions of hours of lost time that could be used by “drivers” (really passengers) to work, play, eat or sleep on their daily commutes. Theoretically, countless lives could be saved by vehicles that are never fatigued, impaired, or distracted — increasing road safety, reducing congestion, and possibly freeing up land currently used for parking lots.

Of the 225 partners developing on the NVIDIA DRIVE PX platform, more than 25 are developing fully autonomous robotaxis using NVIDIA CUDA GPUs. Today, their trunks resemble small data centers, loaded with racks of computers with server-class NVIDIA GPUs running deep learning, computer vision and parallel computing algorithms. Their size, power demands and cost make them impractical for production vehicles.

NVIDIA AI Vehicle Demonstration

The computational requirements of robotaxis are enormous — perceiving the world through high-resolution, 360-degree surround cameras and lidars, localizing the vehicle within centimeter accuracy, tracking vehicles and people around the car, and planning a safe and comfortable path to the destination. All this processing must be done with multiple levels of redundancy to ensure the highest level of safety. The computing demands of driverless vehicles are easily 50 to 100 times more intensive than the most advanced cars today with human drivers.

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The Continuing Importance of GPUs For More Than Just Pretty Pictures

Thursday, March 16th, 2017

While it seems that central processing units (CPUs) get all the glory for computing horsepower, graphical processing units (GPUs) have become the processor of choice for many types of intensively parallel computations.

As the boundaries of computing are pushed in areas such as speech recognition and natural language processing, image and pattern recognition, text and data analytics, and other complex areas, researchers continue to look for new and better ways to extend and expand computing capabilities. For decades this has been accomplished via high-performance computing (HPC) clusters, which use huge amounts of expensive processing power to solve problems.

Researchers at the University of Illinois had studied the possibility of using graphics processing units (GPUs) in desktop supercomputers to speed processing of tasks such as image reconstruction, but it was a computing group at the University of Toronto that demonstrated a way to significantly advance computer vision using GPUs. By plugging in GPUs, previously used primarily for graphics, it became possible to achieve huge performance gains on computing neural networks, and these gains were reflected in superior results in computer vision.

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