System-level exploration of hierarchical storage organizations for embedded data-intensive applications

This paper presents an energy-driven memory management methodology for the system-level exploration of hierarchical storage organizations. Starting from the behavioral specification of a data-intensive signal processing application, the memory management tasks are addressed in a common algebraic framework, using data-dependence analysis techniques similar to those used in modern compilers. This paper focuses on the data assignment to the memory layers and, then, on the signal-to-memory mapping, the target being the reduction of the energy consumption in the hierarchical memory subsystem.