Continental Supercomputer for AI System Training


Source: Continental announcement

FRANKFURT AM MAIN, Germany – In order to develop innovative technologies even more efficiently and quickly, Continental has invested in setting up its own supercomputer for Artificial Intelligence (AI), powered by NVIDIA InfiniBand-connected DGX systems.

The computer has been operating from a datacenter in Frankfurt am Main, Germany, since the beginning of 2020 and is offering computing power as well as storage to developers in locations worldwide. AI enhances advanced driver assistance systems, makes mobility smarter and safer and accelerates the development of systems for autonomous driving.

“The supercomputer is an investment in our future,” said Christian Schumacher, head of Program Management Systems in Continental’s Advanced Driver Assistance Systems business unit. “The state-of-the-art system reduces the time to train neural networks, as it allows for at least 14 times more experiments to be run at the same time.”

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Advanced driver assistance systems use AI to make decisions, assist the driver and ultimately operate autonomously. Environmental sensors like radar and cameras deliver raw data. This raw data is being processed in real-time by intelligent systems to create a comprehensive model of the vehicle’s surroundings and devise a strategy on how to interact with the environment.

Finally, the vehicle needs to be controlled to behave like planned. But with systems becoming more and more complex, traditional software development methods and machine learning methods have reached their limit. Deep Learning and simulations have become fundamental methods in the development of AI-based solutions.

Main use cases: Deep Learning, Simulation and Virtual Data Generation

With Deep Learning, an artificial neural network enables the machine to learn by experience and connect new information with existing knowledge, essentially imitating the learning process within the human brain. But while a child is capable of recognizing a car after being shown a few dozen pictures of different car types, several thousand hours of training with millions of images and therefore enormous amounts of data are necessary to train a neural network that will later on assist a driver or even operate a vehicle autonomously. 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,” said 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. “Our development team has been growing in numbers and experience over the past years. With the supercomputer, we are now able to scale computing power even better according to our needs and leverage the full potential of our developers.”

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 thus simulating physical test drives. With the supercomputer, data can now be generated synthetically, a highly computing power consuming use case that allows systems to learn from travelling virtually through a simulated environment.

The entire announcement can be viewed by clicking HERE.

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