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A Flexible High-Performance Accelerator Platform for Automotive Sensor Applications
Abstract:
High-performance computer architectures for advanced driver assistance systems have become increasingly important in automotive research in the last several years. In order to achieve an optimal and robust perception of the vehicle's surroundings, current driver assistance applications typically rely on multiple sensor systems that deliver large amounts of incoming data from different sensor types. Such sensors include optical systems, which consist of a multi-camera setup combined with complex preprocessing algorithms. These algorithms exhibit high computation and data transport demands, as real-time image processing of multiple input streams is a mandatory requirement for these systems. At the same time, however, future driver assistance systems must adhere to strict power consumption requirements and automotive cost constraints in order to be considered for integration in series vehicles.
This paper addresses these power and cost problems and presents an FPGA-based high-performance computing platform combined with a flexible, weakly-programmable data flow architecture and an associated high-level prototyping framework, which targets an efficient acceleration of computation-intensive tasks in driver assistance applications. The usability and processing performance of the platform is demonstrated by an advanced Motion Estimation application, which represents a challenging preprocessing step in automotive image processing.