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The amount of the data storage in signal processing systems, whose behavior is described by loop-organized algorithmic specifications, has an important impact on the overall energy consumption, chip area, as well as system performance. This paper presents a methodology based on lattices which can be used to address several memory management tasks for applications with high-level specifications, where the main data structures are multidimensional arrays. This methodology was used in the past for the exact computation of the minimum data storage in applications with procedural, affine specifications. The paper discusses two applications of that technique in the memory management of data-dominated signal processing systems: the evaluation of the impact of loop transformations on the data storage, and the assessment and efficient implementation of models.
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