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Nvidia places its might behind self-driving cars by 2021

CEO Jensen Huang says his chip company has no interest in small-time AI projects

Nvidia CEO Jensen Huang

TAIPEI Nvidia CEO Jensen Huang expects fully autonomous vehicles to become mainstream no later than 2021, and the next step for the graphics chipmaker is to roll out a development platform for self-driving technology by the latter half of next year.

"It will take no more than four years to have fully autonomous cars on the road," Huang told reporters in Taipei on Oct. 26. However, he declined to predict when the majority of vehicles would become driverless.

Huang's comments came two weeks after Nvidia introduced its Drive PX Pegasus supercomputer, which it claims will help make possible "Level 5," or fully autonomous, vehicles that can carry out all driving tasks on the road whether or not there is a human on board.

Working on self-driving technology with some 250 carmakers worldwide such as Japan's Toyota Motor, Germany's Audi and America's Ford Motor, as well as ride-hailing companies, Huang said his company is well-positioned to have a central role in the vehicles of the future. Automakers are relying on Nvidia -- in which Japanese tech giant SoftBank's Vision Fund reportedly holds a roughly 5% stake -- to run the data-intensive deep-learning calculations for autonomous driving. But much of this is still in the pilot stage.

Several carmakers and tech companies share Huang's view about the large-scale rollout of fully autonomous vehicles. Germany's BMW, which is working with Intel-controlled Israeli startup Mobileye on self-driving technology, has targeted the production of fully automated automobiles by 2021. Elon Musk, CEO of America's Tesla, embraces an even more aggressive schedule -- he is looking to release self-driving cars under the electricvehicle maker's brand by 2019. Toyota is more skeptical. It sees self-driving vehicles on highways by 2020, and expects more advanced "Level 4" cars to operate in specific areas within a decade.

Tesla, one of Nvidia's partners in self-driving technology, has reportedly turned to rival Intel for in-car information and entertainment, as a way to reduce Tesla's reliance on Nvidia. But Huang said, "Our focus for the auto industries is not car infotainment systems. ... Those are very easy to do and almost everyone can do them. We are very focused on autonomous vehicles. Computation there is very complicated and deep learning is very essential -- and we are very good in these areas."

Despite a growing number of competitors entering the artificial intelligence field, Nvidia has emerged as a tech superstar, with its flagship graphics processors now widely used to accelerate sophisticated AI in data centers, smart cars, infrastructure and industry automation.

Shares of the U.S. chipmaker have nearly tripled over the past 12 months. For the second quarter ended in July, it reported record revenue of $2.23 billion, up 56% year on year. The company's data center sales shot up 175% from the previous year to $416 million, while its automotive business grew 19% on the year to $142 million.

GOING BIG But while leading tech companies such as Apple are adding more AI features such as face and voice recognition for smartphones, Huang said Nvidia will not venture into AI chips for mobile and connected devices.

"I don't really see it," Huang said. "For these simple AI chips for cellphones and [Amazon's voice assistant] Alexa, that's not the area we will focus on." He added that Qualcomm, NXP, Broadcom, Texas Instruments and other peers have already made the chip market for connected devices very crowded.

Huang said Nvidia will only concentrate on segments that require heavy computing workloads, foreseeing a rosy future for the company, especially in automated machine applications for smart cities and robotics.

Huang said his goal is to make AI computing more affordable. Years ago, processing 45,000 images per second would require a data center server to have 160 central processor units, or CPUs, according to Huang. Now, it only takes eight graphics processing units and one-sixth of the original costs to handle the same amount of data.

"Artificial intelligence computation is expensive and we are soon to make it very affordable," Huang said.

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