2001
DOI: 10.1145/375977.375978
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Data and memory optimization techniques for embedded systems

Abstract: We present a survey of the state-of-the-art techniques used in performing data and memory-related optimizations in embedded systems. The optimizations are targeted directly or indirectly at the memory subsystem, and impact one or more out of three important cost metrics: area, performance, and power dissipation of the resulting implementation. We first examine architecture-independent optimizations in the form of code transoformations. We next cover a broad spectrum of optimization techniques that ad… Show more

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Cited by 312 publications
(161 citation statements)
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References 66 publications
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“…Taking, for instance, the linearization by row concatenation, with the rows sorted increasingly, it can be observed that the maximum distance between live elements (black points) is reached when the index vectors of the A-elements are the minimum and, respectively, the maximum relative to the lexicographic order. 1 These array A-elements are represented by the points M = A [2] [7] and N = A [12] [7] in Fig. 4, and dist…”
Section: The Flow Of the Signal Assignment Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…Taking, for instance, the linearization by row concatenation, with the rows sorted increasingly, it can be observed that the maximum distance between live elements (black points) is reached when the index vectors of the A-elements are the minimum and, respectively, the maximum relative to the lexicographic order. 1 These array A-elements are represented by the points M = A [2] [7] and N = A [12] [7] in Fig. 4, and dist…”
Section: The Flow Of the Signal Assignment Algorithmmentioning
confidence: 99%
“…Since data transfer and storage have a significant impact on both the system performance and the major cost parameters-power consumption and chip area, the designer must spend a significant effort during the system development process on the exploration of the possible memory organizations in order to achieve a cost-optimized design [2,3]. This is why the problem of memory allocation is central to any computer-aided design tool focusing on memory management.…”
Section: Introductionmentioning
confidence: 99%
“…[5,14] for good tutorial overviews). All these techniques are complementary to our work and are applicable in the part of the Java code that accesses static data in the dynamic applications under study.…”
Section: Related Workmentioning
confidence: 99%
“…In the case of static memory, Benini et al [4] and Panda et al [24] presented in the last decade two thorough surveys on static data and memory optimization techniques for embedded systems. More recently, in [6], [10] and [7], authors achieve to reduce the memory subsystems requirements by 50% using a linear time algorithm by exploring a coordinated data and computation reordering for array-based data structures in multimedia applications.…”
Section: Introductionmentioning
confidence: 99%
“…In this context, MOEA-based optimization has been applied to solve linear-and nonlinear problems by exploring the entire state space in parallel. Thus, it is possible to perform optimization in non convex regular functions, and to select the order of algorithmic transformations in concrete types of source codes [24]. However, such techniques are not applicable to DDT implementations due to it is not possible to know the DDT behavior (the number of elements stored in the DDT, number of read accesses, number of write accesses, etc.…”
Section: Introductionmentioning
confidence: 99%