Mapping model with Inter-array memory sharing for multidimensional signal processing

The storage requirements in data-intensive signal processing systems (including applications in video and image processing, artificial vision, medical imaging, real-time 3-D rendering, advanced audio and speech coding) have an important impact on both the system performance and the essential design parameters -- the overall power consumption and chip area. This is due to the significant amount of data that must be stored during the execution of the algorithmic specification, as well as due to the amount of data transfers to/from large, energy-consuming, off-chip data memories. This paper addresses the problem of efficiently mapping the multidimensional signals from the algorithmic specification of the system into the physical memory. Different from all the previous mapping models that aim to optimize the memory sharing between the elements of a same array, creating separate windows in the physical memory for distinct arrays, this proposed mapping model is the first one to exploit the possibility of memory sharing between different arrays. As a consequence, this signal-to-memory mapping approach yields significant savings in the amount of data storage resulted after mapping.