Optimization model for memory bandwidth usage in X-ray image enhancement

In Cardiovascular minimal invasive interventions, physicians require low-latency X-ray imaging applications, as their actions must be directly visible on the screen. The image-processing system should enable the simultaneous execution of a plurality of functions. Because dedicated hardware lacks flexibility, there is a growing interest in using off-the-shelf computer technology. Because memory bandwidth is a scarce parameter, we will focus on optimization methods for bandwidth reduction within multiprocessor systems at the chip level. We create a practical realistic model of required compute and memory bandwidth for a given set of image-processing functions. Similar modeling is applied for the available system resources. We concentrate in particular on X-ray image processing based on multi-resolution decomposition, noise reduction and image-enhancement techniques. We derive formulas for which we can optimize the mapping of the application onto processors, cache and memory for different configurations. The data-block granularity is matched to the memory hierarchy, so that caching will be optimized for low latency. More specifically, we exploit the locality of the signal-processing functions to streamline the memory communication. A substantial performance improvement is realized by a new memory-communication model that incorporates the data dependencies of the image-processing functions. Results show a memory-bandwidth reduction in the order of 60% and a latency reduction in the order of 30-60% compared to straightforward implementations.