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Nvidia Shows Off New AI Computer For Baidu’s Self-Driving Car

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Michelle Toh
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Michelle Toh
Michelle Toh
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September 13, 2016, 1:17 AM ET
CHINA-ENTERTAINMENT-LEISURE-CHINAJOY
This photo taken on July 28, 2016 shows visitors crowded around the booth of NVIDIA during 2016 China Digital Entertainment Expo, known as "ChinaJoy", in Shanghai. The four-day show, which showcases online games, console games, web games, hardware products related to digital entertainment kicked off on July 28 at the Shanghai New International Expo Center. / AFP / STR / China OUT (Photo credit should read STR/AFP/Getty Images)STR/ AFP/Getty Images/File
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U.S. chipmaker Nvidia (NVDA) showed off on Monday a smaller and more efficient artificial intelligence computer for self-driving cars, saying it would power Baidu’s (BIDU) mapping and autonomous vehicle technology.

Chinese web services company Baidu will deploy Nvidia‘s new Drive PX 2 as its in-vehicle car computer for its self-driving system, Nvidia said in a press release as it unveiled the computer at the GPU Technology Conference in Beijing.

As more carmakers develop plans for self-driving technology to roll out in their vehicles in the next decade or less, Nvidia is trying to lower the barriers to entry, providing powerful computers to help automakers enter the market.

See also: Baidu and Nvidia to Build Artificial Intelligence Platform for Self-Driving Cars

Earlier this month, Nvidia and Baidu announced a partnership to develop a full self-driving car architecture from the cloud to the vehicle using both companies’ expertise in artificial intelligence (AI).

Nvidia said its new Drive PX 2 computer uses 10 watts of power and is half the size of the original version, launched in January. That solves a problem faced by carmakers incorporating self-driving technology – how to pack the punch of AI, which helps cars make decisions, into a compact computer suitable for production-ready vehicles.

For more on Baidu, watch Fortune’s video:

Configured with a single processor, the Drive PX 2 fuses incoming data from sensors and uses deep neural networks to produce a complex picture of objects around a vehicle.

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