Continental Puts Its Own Supercomputer for Vehicle AI System Training, Powered by NVIDIA DGX, Into Operation

July 28, 2020 Tiera Oliver

Continental has invested in setting up its own supercomputer for Artificial Intelligence (AI), powered by NVIDIA InfiniBand-connected DGX systems, offering computing power as well as storage to developers in locations worldwide.

Continental’s supercomputer is built with more than 50 NVIDIA DGX systems, connected with the NVIDIA Mellanox InfiniBand network. According to the company, it is ranked according to the publicly available list of TOP500 supercomputers as the top system in the automotive industry. A hybrid approach has been chosen to be able to extend capacity and storage through cloud solutions if needed.

Several thousand hours of training with millions of images and data are necessary to train a neural network that will later on assist a driver or even operate a vehicle autonomously. According to the company, the NVIDIA DGX POD not only reduces the time needed for this complex process, it also reduces the time to market for new technologies.

“Overall, we are estimating the time needed to fully train a neural network to be reduced from weeks to hours,” says Balázs Lóránd, head of Continental’s AI Competence Center in Budapest, Hungary, who also works on the development of infrastructure for AI-based innovations together with his groups in Continental.

To date, the data used for training those neural networks comes mainly from the Continental test vehicle fleet. Currently, they drive around 15,000 test kilometers each day, collecting around 100 terabytes of data, equivalent to 50,000 hours of movies. Already, the recorded data can be used to train new systems by being replayed and therefore simulating physical test drives. With the supercomputer, data can now be generated synthetically.

This can have several advantages for the development process: Firstly it might make recording, storing, and mining the data generated by the physical fleet unnecessary, as necessary training scenarios can be created on the system itself. Secondly, it increases speed, as virtual vehicles can travel the same number of test kilometers in a few hours that would take a real car several weeks. Thirdly, the synthetic generation of data makes it possible for systems to process and react to changing and unpredictable situations. Overall, this will allow vehicles to navigate safely through changing and extreme weather conditions or make reliable forecasts of pedestrian movements.

The supercomputer is located in a datacenter in Frankfurt. Certified green energy is being used to power the computer, with GPU clusters being energy efficient.

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About the Author

Tiera Oliver, edtorial intern for Embedded Computing Design, is responsible for web content edits as well as newsletter updates. She also assists in news content as far as constructing and editing stories. Before interning for ECD, Tiera had recently graduated from Northern Arizona University where she received her B.A. in journalism and political science and worked as a news reporter for the university's student led newspaper, The Lumberjack.

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