Continental brings its own supercomputer for vehicle AI system training

Continental has invested in setting up its own supercomputer for Artificial Intelligence (AI), powered by NVIDIA InfiniBand-connected DGX systems. It 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. AIenhances advanced driver assistance systems, makes mobility smarter and safer and accelerates the development of systems for autonomous driving.

Christian Schumacher, Head of Program Management Systems in Continental’s Advanced Driver Assistance Systems Business Unit, says, “The supercomputer is an investment in our future. 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.”

The supercomputer is located in a datacenter in Frankfurt, which has been chosen for its proximity to cloud providers and, more importantly, its AI-ready environment, fulfilling specific requirements regarding cooling systems, connectivity and power supply. Certified green energy is being used to power the computer, with GPU clusters being much more energy efficient than CPU clusters by design. 

Cooperation with NVIDIA secures top quality

Manuvir Das, head of Enterprise Computing at NVIDIA, comments, “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.”

IT masterpiece for AI based solutions

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. 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.