Bromberger, MichaelEhrle, SteffenScharrer, MichaelErlinghagen LukasSchick, Jens2020-03-112020-03-112017https://dl.gi.de/handle/20.500.12116/31940Calculating distances from objects to a subject, for instance a car, is a central task in many applications. Such distances can be calculated by stereo vision exploiting stereo camera images. The high complexity of this approach, which has to be performed under high-performance and lowpower constraints, limits a wide usage. Hardware acceleration is a promising solution to meet above constraints. Two main approaches exist, local ones work on a pixel-wise scheme and global ones consider all pixels at the same time, which highly increases the memory and time complexity. Several optimization methods exist to find Pareto-optimal designs in the design space spanned by accuracy, performance, and resource consumption. Besides well-known techniques, we design, implement, and evaluate new methods, which includes the current research trend of approximate computing. Therefore, in this paper we evaluate different optimization techniques on an OpenCL level for local as well as semi-global approaches. While we target on resource reduction for local approaches, we tackle the memory issue of semi-global approaches. We implement all methods on a low-power and low-cost FPGA-based system on chip and evaluate them on available benchmarks as well as on a real-world scenario. The novel semi-global approximate computing design provides a high frame rate, supports a high number of disparities, and achieves a good accuracy on typical traffic scenes.enText/Journal Article0177-0454