The NVIDIA Blog Jen-Hsun On GeForce GTX 970 Posted: 24 Feb 2015 09:10 AM PST  Hey everyone, Some of you are disappointed that we didn't clearly describe the segmented memory of GeForce GTX 970 when we launched it. I can see why, so let me address it. We invented a new memory architecture in Maxwell. This new capability was created so that reduced-configurations of Maxwell can have a larger framebuffer – i.e., so that GTX 970 is not limited to 3GB, and can have an additional 1GB. GTX 970 is a 4GB card. However, the upper 512MB of the additional 1GB is segmented and has reduced bandwidth. This is a good design because we were able to add an additional 1GB for GTX 970 and our software engineers can keep less frequently used data in the 512MB segment. Unfortunately, we failed to communicate this internally to our marketing team, and externally to reviewers at launch. Since then, Jonah Alben, our senior vice president of hardware engineering, provided a technical description of the design, which was captured well by several editors. Here's one example from The Tech Report. Instead of being excited that we invented a way to increase memory of the GTX 970 from 3GB to 4GB, some were disappointed that we didn't better describe the segmented nature of the architecture for that last 1GB of memory. This is understandable. But, let me be clear: Our only intention was to create the best GPU for you. We wanted GTX 970 to have 4GB of memory, as games are using more memory than ever. The 4GB of memory on GTX 970 is used and useful to achieve the performance you are enjoying. And as ever, our engineers will continue to enhance game performance that you can regularly download using GeForce Experience. This new feature of Maxwell should have been clearly detailed from the beginning. We won't let this happen again. We'll do a better job next time. Jen-Hsun The post Jen-Hsun On GeForce GTX 970 appeared first on The Official NVIDIA Blog.    Here’s How Deep Learning Will Accelerate Self-Driving Cars Posted: 21 Feb 2015 09:59 AM PST  Punch Buggy. Slug Bug. The names differ, but we've all played this game. See a Volkswagen Beetle, punch your sibling. Deep learning works the same way. Just with more math—and fewer bruises. Deep learning refers to algorithms—step-by-step data-crunching recipes—for teaching machines to understand "unstructured data." That's another way of saying information that lives outside of spreadsheets and databases. For example, images, speech and video. Because of the GPU's key role in all these technologies, our GPU Technology Conference, which runs March 17-20 in Silicon Valley, is a great place to learn more. GTC will feature more than two dozen talks focused on how GPUs are changing the auto industry. So, how does deep learning work? A great way to understand it is to look at NVIDIA DRIVE, our new auto-pilot car computer. When paired with computer vision technology—powered by our NVIDIA Tegra processors—DRIVE gives vehicles an uncanny level of self-awareness.  NVIDIA DRIVE products promise to power pixels inside the car, and sensors mounted outside it. To show what DRIVE can do, NVIDIA engineers stuck video cameras onto their cars to capture 40 hours of video. They then used Amazon Mechanical Turk to have frames manually tagged by people to categorize about 68,000 objects in the footage. Training Artificial Brains Our engineers then fed these images to servers equipped with powerful GPUs that form an artificial neural network. It's a process computer scientists call "training." It lets a neural network learn to see patterns and recognize objects. It's a little like how children learn. Parents, friends and punch-happy older siblings identify objects in the world for the child. Then the child's brain learns how to identify these objects in a broad array of situations. This is where the "deep" in deep learning comes in. With deep learning, a neural network learns many levels of abstraction. They range from simple concepts to complex ones. Each layer categorizes some kind of information. It then refines it and passes it along to the next.  DRIVE can pick out a different kinds of vehicles. It can even spot a police car trailing several car lengths behind. Deep learning stacks these layers. This lets a machine learn what computer scientists call a "hierarchical representation." So, the first layer might look for edges. The next layer looks for collections of edges that form angles. The next might look for patterns of edges. After many layers the neural network learns the concept of, say, a pedestrian crossing the street. GPUs are ideal for this. They can cut the time that it takes to train these neural networks to just days from a year or more. GPUs perform many calculations at once. That makes GPUs great for neural nets, which sort unstructured data like images. Once a system is "trained," that learning can be used in applications like self-driving cars. Precision Vision It's technology that's arriving just in time. Low-cost cameras and sensors are giving cars the ability to suck in huge amounts of information. NVIDIA's advanced computer vision technology turns that data into 3D maps that vehicles can use to navigate the world around them. Deep learning takes those capabilities to another level. Our Tegra X1-powered NVIDIA DRIVE system takes advantage of the models that neural networks create. That lets NVIDIA DRIVE understand the world the way human drivers do.  DRIVE is built to turn the information sucked up by sensors mounted all around a car into self-awareness. As a result, NVIDIA DRIVE can tease out information fast. DRIVE can pick out different kinds of vehicles. It can discern a police car from a taxi; an ambulance from a delivery truck; or a parked car from one that is about to pull out into traffic. That capability isn't limited to vehicles. NVIDIA DRIVE can identify everything from cyclists on the sidewalk to absent-minded pedestrians. Deep learning can even categorize images that challenge human eyes. Even in bad weather, DRIVE can read flickering electronic signs. Or spot brake lights. Or recognize a Volkswagen Beetle. Trust us: never play Punch Buggy with a GPU. The post Here's How Deep Learning Will Accelerate Self-Driving Cars appeared first on The Official NVIDIA Blog.   You are subscribed to email updates from The Official NVIDIA Blog Email delivery powered by Google Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, United States |