Optimization methodology of dynamic data structures based on genetic algorithms for multimedia embedded systems

Baloukas, Christos; Risco‐Martín, José L.; Atienza, David; Poucet, Christophe; Papadopoulos, Lazaros; Mamagkakis, Stylianos; Soudris, Dimitrios; Hidalgo, J. Ignacio; Catthoor, Francky; Lanchares, Juan

doi:10.1016/j.jss.2008.08.032

Cited by 17 publications

(15 citation statements)

References 12 publications

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“…These need to come first since they can completely alter the design of the application and the existing DDTs. The can be decoupled of the other subsequent steps by introducing high-level estimators [18].…”

Section: Methods Overviewmentioning

confidence: 99%

“…DDT optimizations are not discussed further in this book as they are not part of the core research of this book. Instead I refer the reader to [16,18]. Again, high-level estimators are used to decouple this step from subsequent steps [18].…”

Section: Methods Overviewmentioning

confidence: 99%

“…More specifically, in [18], it is shown how communication protocol applications contain data structures that are dynamic in nature. More specifically, packet scheduling algorithms have to deal with a variety of queues of packets, and these are by definition dynamic in nature, as it is not known in advance how many packets will fall into each scheduling queue.…”

Section: Potential For Optimizationsmentioning

confidence: 99%

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Dynamic Memory Management for Embedded Systems

Alonso¹,

Mamagkakis

Poucet³

et al. 2015

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Section: Methods Overviewmentioning

confidence: 99%

Section: Methods Overviewmentioning

confidence: 99%

Section: Potential For Optimizationsmentioning

confidence: 99%

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Dynamic Memory Management for Embedded Systems

Alonso¹,

Mamagkakis

Poucet³

et al. 2015

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“…Our objective is to obtain a set of data structure with minimizing memory accesses, memory footprint and energy consumption for each variable in the mobile application, the result (a solution) is represented by a set of pairs (c, s) {c i ∈ C, s j ∈ S}, where, C is containers, S is a set of data structures proposed by Baloukas et al [10], i.e. S={AR, AR_P, DLL, DLL_O, SLL, SLL_O, DLL_AR, SLL_AR}, where, AR is array, AR_P is array of pointers, DLL is double-link list, DLL_O is double-link list with roving pointer, SLL is single link list, SLL_O is single link list with roving pointer, DLL_AR is double link list of arrays, and SLL_AR is single link list of arrays.…”

Section: Multi-objective Optimization Model For Mobile Multimedia Appmentioning

confidence: 99%

“…Jiang et al [3] proposed a method of low power consumption data structure design for embedded applications, but it employed handwork optimization to applications without evolution algorithm. Risco-Martin et al [4,10] used multiObjective evolutionary algorithms such as genetic algorithm (GA) to optimizing data structures for embedded applications, but this can generate issues such as the premature converge of the population to a local optimum or general loss of diversity.…”

Section: Introductionmentioning

confidence: 99%

Mobile Multimedia Applications Optimization at Dynamic Data Process Level

Xiaosheng¹,

Yu²,

Guo³

et al. 2017

IJMUE

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A review of bioinspired computer‐aided design tools for hardware design

Lanchares¹,

Garnica²,

Fernández-de-Vega

et al. 2013

Concurrency and Computation

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During the last two decades, bioinspired techniques have emerged as a powerful optimization and problem solving tool for a wide range of real world applications. Parallel architectures and computer-aided design tools have been also influenced by this evolutionary fashion. In this paper, we give a broader view of the application of techniques inspired by nature to hardware design and parallel architectures problem solving. Our aim is to furnish an overview of the various bioinspired techniques based tools that have been used so far to solve the problems of automatic hardware design. We can claim that a lot of the approaches found in the literature suffer from a lack of interdisciplinary interaction among researchers of both evolutionary computation and hardware design fields. In addition, we have also detected that some multi-objective problems do not use the appropriate algorithms.Among widely known heuristics, gradient algorithms, such as Hill Climbing, were soon employed. These algorithms start the search process from a single point, stochastically generated from the solution space, and then perform a search process using the direction of the gradient towards the objective function. These algorithms are of an easy implementation and relatively efficient. However, local optima are usually the output and no general procedures exist to know the distance from the global optimum.An improvement over the previous approach was obtained by the simulated annealing (SA) algorithm [2], capable of removing some of the previously described disadvantages. SA begins with a randomly generated candidate solution-a point in the search space-with some specific features, and then, the algorithm proceeds by applying a disturbance to the candidate solution, thus trying to improve it. If the solution improves, it is selected and becomes the next candidate solution; otherwise, a probability decides whether to continue with the initial candidate or with the new one. The probability of accepting a worse solution depends on the system temperature-a fundamental parameter for SA. The system starts with a very high temperature, so most of the worse solutions are accepted at the beginning. The temperature is then lowered gradually; the lower the temperature, the lower the probability of accepting a worse solution. So far, SA has been the most frequently employed heuristic in computer-aided design (CAD) tools.Nevertheless, a new kind of heuristic has gained popularity: bioinspired algorithms (BAs) [3]. Among BAs, evolutionary algorithms (EAs) try to emulate the natural evolution of species, following Darwin's principles of natural selection. One of their main advantages is the employment of a set (population) of candidate solutions (individuals) at the same time, which gives rise to an intrinsically parallel search process. We can also find other bioinspired approaches on the basis of the social behavior of the species such as particle swarm optimization (PSO) [4] or ant colony optimization (ACO) [5] algorithms.This review focuses on the appli...

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Optimization methodology of dynamic data structures based on genetic algorithms for multimedia embedded systems

Cited by 17 publications

References 12 publications

Dynamic Memory Management for Embedded Systems

Dynamic Memory Management for Embedded Systems

Mobile Multimedia Applications Optimization at Dynamic Data Process Level

A review of bioinspired computer‐aided design tools for hardware design

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