Jens Harnisch

A 100-GOPS programmable processor for vehicle vision systems

New generations of automobiles will include driver assistance systems requiring powerful, low-cost processors to handle video/camera applications and to enable fast, convenient application development. Shrinking feature sizes on processors already in development will bring substantial increases in system speed and functionality.

100 GOPS vision processor for automotive applications

Car vision systems for the improvement of drivers comfort and safety will be introduced in the next car generation, with an exponential market growth in the next years. The Corporate Research division of lnfineon Technologies AG has developed a fully programmable vision processor, which fulfills the requirements of low power, low system costs, and high computational performance (e.g., robust approaches for object detection based on stereo processing need a performance of more than 15 GIPS).The prototype currently available achieves a peak performance of 53 GOPS. With a redesign in 0.13 μm CMOS technology, the performance increases to more than 100 GOPS, while the power consumption is less than 500 mW and the die size reduces to 70 mm2.

Model-Based Analysis for Safety Critical Software

Safety-relevant software developed within the automotive domain is subject to the safety standard ISO 26262. In particular, a supplier must show that implemented safety mechanisms sufficiently address relevant failure modes. This involves complex and costly testing procedures. We introduce an early analysis approach for safety mechanisms implemented in safety-relevant software by combining model checking and model-based testing. Model checking is applied to verify the correctness of an abstract amodel of the system under test. The verified model is then used to automatically generate tests for the verification of the implemented Safety Elements. The approach has been evaluated in an industrial case study, addressing Analogue Digital Converters as part of the motor control within a hybrid electric vehicle. The results suggest that our approach allows to create high quality test suites. In addition, the test model helps to reduce misunderstandings due to imprecise specification of safety mechanisms.