2013
DOI: 10.1007/978-3-642-32548-9_35
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A Comparison of Multi-objective Algorithms for the Automatic Design Space Exploration of a Superscalar System

Abstract: Abstract. In today's computer architectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) techniques. We developed the Framework for Automatic Design Space Exploration (FADSE) which is focused on microarchitectural optimizations. This framework includes several state-of-the art heuristic algorithms. In this paper we selected three of them, NSGA-II and SPEA2 as genetic algorithms as … Show more

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Cited by 12 publications
(12 citation statements)
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References 16 publications
(24 reference statements)
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“…SPEA-2 seems to provide better solutions in terms of convergence and diversity but at the expense of computational time [106]. However, a clear ultimate winner between NSGA-II and SPEA2 cannot be determined [107], so both methods can be considered equivalent.…”
Section: Multi Objectivementioning
confidence: 99%
“…SPEA-2 seems to provide better solutions in terms of convergence and diversity but at the expense of computational time [106]. However, a clear ultimate winner between NSGA-II and SPEA2 cannot be determined [107], so both methods can be considered equivalent.…”
Section: Multi Objectivementioning
confidence: 99%
“…Since the optimisation problem addresses multiple objectives such as performance and area, there is usually no single solution, but a Pareto-set of non-dominated solutions. An introduction to DSE techniques and a comparison of optimisation algorithms can be found in [20,21]. In this framework, the strength of the countermeasures against SCA, together with the choice of a specific cryptographic algorithm, can be counted as a parameter exposed by the platform.…”
Section: Sca Countermeasuresmentioning
confidence: 99%
“…Since the optimisation problem addresses multiple objectives such as performance and area, there is usually no single solution, but a Pareto‐set of non‐dominated solutions. An introduction to DSE techniques and a comparison of optimisation algorithms can be found in [20, 21].…”
Section: Extending the Design Space With The Security Dimensionmentioning
confidence: 99%
“…Empirical studies in recent years have shown that the PSO algorithms achieved high convergence speed for the multiobjective optimization problems [33]. Apart from this, it has been performed to apply to the automatic space exploration on the superscalar computer systems successfully [34,35]. More notably, it is suggested that PSO has been exercised to greatly improve the implicit SML algorithm through convergence speed and accuracy in consideration of the financial model calibration problems.…”
Section: Introductionmentioning
confidence: 99%