Hannover, Germany -- October 25, 2021 – Today, videantis, a leading supplier of deep learning, computer vision, image processing and video coding solutions, announced availability of its technological platform for fail-operational processing at reduced cost for both smart sensors as well as highly integrated central ECUs. The concept for this solution was developed within the European PRYSTINE (Programmable Systems for Intelligence in Automobiles) project.
For three years, and equipped with a budget of € 50 Million, about 60 project partners were collaborating to build a Fail-operational Urban Surround perceptION (FUSION) based on Radar and LiDAR sensor fusion and control functions, eventually enabling safe automated driving in urban and rural environments.
The output of this project helps to address advanced functional safety requirements on embedded videantis-based multiprocessor systems up to ISO26262 ASIL D. Compared to traditional lockstep architectures, more than 50% of the cost can be saved due to the reduction of the silicon area.
The cost reduction is achieved by run-time failure detection schemes comprising of core self-test modules and a result monitoring software layer (RMSL) applied to the fine-grain and highly scalable videantis multiprocessor system. With this, faults can be detected during runtime and processing can be continued while excluding any faulty resource, without the need for duplicate hardware. E.g., with a silicon area overhead of only 3%, a multiprocessing system comprising 32 videantis cores can be turned into a fail-operational processing platform.
“PRYSTINE covers the most important aspects of autonomous driving: performance, efficiency and especially safety. We're proud to have contributed with our highly versatile processing platform which allows for the most cost-efficient way to implement fail-operational functionality”, says Dr. Hans-Joachim Stolberg, CEO/CTO of videantis.
videantis v-MP6000UDX processing platform is highly suited for fail-operational applications such as highly automated driving. With its scalability, it can cover the full range from smart image, Radar, LiDAR sensors (1 to 16 cores) to high-performance AI inference computers (>100 cores). The unified architecture allows the implementation of various functions: video coding, image or graphics processing, computer vision, deep learning using a multitude of network topologies, or even control functions. Utilizing redundancy, self-test and other control mechanisms enables customers to build safe systems according to ISO26262 up to ASIL D, using less silicon space or hardware overhead than conventional lockstep architectures.
About videantis
Headquartered in Hannover, Germany, videantis GmbH is a leading supplier of deep learning and computer vision solutions based on its unified processing platform. With its processor IP, hardware/software-based solutions for deep learning, computer vision, image processing and video coding, as well as its development tools, videantis globally supports semiconductor manufacturers, automotive OEMs and tier 1 suppliers together with customers in other high-volume embedded markets. videantis has been recognized with the Red Herring Award and multiple Deloitte Technology Fast 50 Awards as one of the fastest growing technology companies in Germany.
For more information, please visit https://www.videantis.com.
About PRYSTINE
PRYSTINE (Programmable Systems for Intelligence in Automobiles) is a research and innovation project which is co-funded by the ECSEL JU (Electronic Components and Systems for European Leadership, Joint Undertaking) and the national governments of the ECSEL member states. Members of the PRYSTINE consortium comprise 60 partners from 14 countries including automotive OEMs, semiconductor manufacturers, technology partners and research institutes. The goal of PRYSTINE is to deliver a fail-operational sensor-fusion architecture, integrated with a safe AI framework for object recognition, scene understanding and decision making, supporting the transition to highly automated vehicles.
For more information, please visit https://prystine.eu.
