Baidu Utilizing Inspur’s NF5568M4 Server For Autonomous Car Deep-Learning Platform Support

Originally published on CleanTechnica.

We’ve previously reported on the Chinese internet mega-firm Baidu’s work to develop an autonomous vehicle.

New reports have now revealed that Baidu is utilizing the Chinese IT manufacturer Inspur’s NF5568M4 server to offer hardware support to the deep learning platform of the company’s autonomous car.

Baidu logo

Notably, Inspur is currently in a strategic partnership with the top visual computation company in the world, NVIDIA, that pertains to GPU heterogeneous computing.

Green Car Congress provides more:

In December 2015, Baidu’s driverless car passed a road test starting from the Baidu Tower in Zhongguancun High-tech Park in Beijing, driving onto the G7 Beijing-Xinjiang Expressway, through the Fifth Ring Road and reaching Olympic Forest Park, and back. The driverless technology adopted throughout the course included decelerating, lane switching, overtaking, going on and off ramps and turning around. The car passed each of these testing criteria and reached a top speed of 100 km/h (62 mph).

Image recognition requires hundreds of thousands — in some cases even billions — of learning samples to train a model. A GPU made up of thousands of smaller and more energy-saving cores has therefore become the main force for the application of image recognition training.

…The NF5568M4 co-processing acceleration server is based on the Intel Haswell E5-2600v3 platform combine with four Nvidia Tesla K40 GPUs. The highest single computing capacity of a single GPU server reaches 17 teraflops. Presently, in the KITTIKITTI test suite, Baidu has reached a recognition accuracy of 90% — due to a big contribution by Inspur’s GPU co-processing acceleration server NF5568M4.

The company is currently planning to establish “demonstration regions” within China in 10 different cities — these regions will then see commercial ply utilized autonomous cars tested in real-world conditions. The company is apparently planning for “large-scale” production within only 5 years.

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