NVDA NVIDIA Corporation

SAN JOSE, Calif.—Nvidia Corp. is stepping up plans to expand beyond computer graphics into the field of artificial intelligence, unveiling an unusual processor for the purpose and a computer that uses it to solve scientific problems at extremely high speed.

The company on Tuesday said the new Tesla P100 chip, designed for use in corporate data centers, achieves very high performance by packing 15 billion transistors on a piece of silicon. That is roughly twice as many as Nvidia's prior high-end graphics processor and some new server chips Intel Corp. announced last week.

"It's the largest chip that has ever been made," said Jen-Hsun Huang, Nvidia's chief executive, during a speech kicking off the company's annual technology conference here.

He predicted the chip would initially be purchased by unidentified cloud computing services and next year would begin arrive in servers sold by other companies. Meanwhile, Nvidia plans to offer its own $129,000 computer that comes with eight Tesla P100 chips and software for artificial intelligence applications.

Mr. Huang said the DGX-1, as the new computer is called, can process artificial intelligence tasks as rapidly as 250 servers powered by general-purpose chips like Intel's—an installation that would cost much more. A typical chore that would take 150 hours on one standard server, Mr. Huang said, would take two hours on the DGX-1.

Nvidia, based in Santa Clara, Calif., has been on a multiyear crusade to find users beyond videogames for the chips called GPUs, for graphics processing units. The chips, which feature hundreds of simple processors—compared with between one and 22 large calculating engines found on a typical microprocessor—are already used to solve scientific problems in many large supercomputers.

Mr. Huang recently has focused on artificial intelligence, especially a technique called machine learning that is useful for recognizing images and spoken language. Where conventional image-recognition requires programmers to explicitly define the characteristics of a face, machine-learning software enables computers to learn to pick out faces by surveying huge quantities of so-called training photos.

Nvidia already sells GPUs and special-purpose computers that use machine learning to help cars map their surroundings, detect hazards and drive on their own. Among other announcements at the event Tuesday, the company said its hardware would be used by entrants in an event called Roborace that is designed to test autonomous sports cars.

Mr. Huang said Massachusetts General Hospital would be one of the first customers for its DGX-1 system, which will help analyze around 10 billion medical images to help study disease and devise new treatments.

Companies are trying other kinds of chips to accelerate machine-learning jobs. International Business Machines Corp., for example, in 2014 announced a chip designed to function more like the human brain than other processors.

Patrick Moorhead, an analyst at Moor Insights & Strategy, noted that other companies are coming up with original chip designs or using chips that can be electrically configured after they leave the factory. But he said Nvidia is likely to have an impact on fields where speed is important.

"This means instead of waiting for a day to train the system, you could do that in hours," he said.

Nvidia is also betting heavily on virtual reality to drive demand for GPUs. Mr. Huang on Tuesday demonstrated a new technology called Iray that uses hardware in data centers to help generate 3-D landscapes than look much more like photographic imagery than today's VR systems.

Write to Don Clark at don.clark@wsj.com

(END) Dow Jones Newswires

April 05, 2016 20:55 ET (00:55 GMT)

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