In real-time data-intensive multimedia processing applications, data transfer and storage significantly influence, if not dominate, all the major cost parameters of the design space - namely power consumption, performance, and chip area. This paper presents an electronic design automation (EDA) methodology for the high-level design of hierarchical memory architectures in embedded data-intensive applications, mainly in the area of multidimensional signal processing. This framework employs a formal model operating with integral polyhedra, using techniques specific to the data-dependence analysis employed in modern compilers. Different from the previous works, the problems of data assignment to the memory layers and of banking the on-chip memory are addressed in a consistent way, based on the same formal model. The main design target is the reduction of the static and dynamic energy consumption in the memory subsystem, but the same formal model and algorithmic flow can be also applied to reduce the overall time of access to memories.
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