System-level data-flow transformations for power reduction in image and video processing

Application studies in the domain of image and video processing systems indicate that up to 80% of the power and area cost in customized architectures for such data-dominant processing is due to storage and transfers for multi-dimensional (M-D) data. This paper has two main contributions. First, to reduce this dominant cost, we propose a formalized methodology of data-flow transformations which address the system-level storage organization. Secondly, we will demonstrate the usefulness of the stages in this novel approach based on realistic test-vehicles, i.e. crucial modules in a complex H.263 video decoder system for teleconferencing