In order to develop innovative technologies even more efficiently and quickly in line with shorter development cycles in the automotive industry, Continental AG has announced that it has invested in setting up its own supercomputer for Artificial Intelligence (AI). Powered by NVIDIA InfiniBand-connected DGX systems, the supercomputer has been operating from a datacenter in Frankfurt am Main, Germany, since the start of this year, and is offering computing power as well as storage to developers in locations across the world. The German tire and technology giant points out that 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.”
He explained that the project was set up with an ambitious timeline and implemented in less than a year. After intensive testing and scouting, Continental selected NVIDIA, which powers many of the fastest supercomputers around the world, as a partner.
Manuvir Das, head of Enterprise Computing at the American multinational technology company, said that NVIDIA DGX systems give innovators like Continental AI supercomputing in a cost-effective, enterprise-ready solution that’s easy to deploy. “Using the InfiniBand-connected NVIDIA DGX POD for autonomous vehicle training, Continental is engineering tomorrow’s most intelligent vehicles, as well as the IT infrastructure that will be used to design them,” Mr Das said.
Continental’s supercomputer is built with more than 50 NVIDIA DGX systems, connected with the NVIDIA Mellanox InfiniBand network. 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.
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, which is 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. Continental notes that 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.
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. 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,” according to 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.
The data used for training those neural networks currently is drawn mainly from the Continental test vehicle fleet. The drive around 15,000 test kilometers each day, collecting around 100 terabytes of data – equivalent to 50,000 hours of movies. 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.
Frankfurt was chosen as the location for the supercomputer due to its proximity to cloud providers and, more importantly, its AI-ready environment, fulfilling specific requirements regarding cooling systems, connectivity and power supply. Continental says certified green energy is being used to power the computer.