Efficient symbolic multi-objective design space exploration

Lukasiewycz,; Haubelt, Christian; Teich,

doi:10.1109/aspdac.2008.4484040

Cited by 52 publications

(33 citation statements)

References 18 publications

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“…This novel approach known as SAT decoding has been presented in [9]. It is known to be superior to common methods that are based on ILPs or MOEAs only [10].…”

Section: Optimizationmentioning

confidence: 99%

Concurrent topology and routing optimization in automotive network integration

Lukasiewycz¹,

Glaß²,

Haubelt³

et al. 2008

Proceedings of the 45th Annual Design Automation Conference

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In this paper, a novel automatic approach for the concurrent topology and routing optimization that achieves a high quality network layout is proposed. This optimization is based on a specialized binary Integer Linear Program (ILP) in combination with a Multi-Objective Evolutionary Algorithm (MOEA). The ILP is formulated such that each solution represents a topology and routing that fulfills all requirements and demands of the network. Thus, in an iterative process, this ILP is solved to obtain feasible networks whereas the MOEA is used for the optimization of multiple even non-linear objectives and ensures a fast convergence towards the optimal solutions. Additionally, a domain specific preprocessing algorithm for the ILP is presented that decreases the problem complexity and, thus, allows to optimize large and complex networks efficiently. The experimental results validate the performance of this methodology on two state-of-the-art prototype automotive networks.

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“…This novel approach known as SAT decoding has been presented in [9]. It is known to be superior to common methods that are based on ILPs or MOEAs only [10].…”

Section: Optimizationmentioning

confidence: 99%

Concurrent topology and routing optimization in automotive network integration

Lukasiewycz¹,

Glaß²,

Haubelt³

et al. 2008

Proceedings of the 45th Annual Design Automation Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, most design spaces have multiple, often non-linear objectives. For being able to apply SAT solvers efficiently to multiobjective optimization problems, Lukasiewycz et al propose SAT decoding [9], which couples a Multi-objective Evolutionary Algorithm (MOEA) with a SAT solver, as illustrated in Fig. 4.…”

Section: B Sat Decoding For Dsementioning

confidence: 99%

“…To achieve this, the SAT-decoding approach [9] is incorporated which is a heuristic using symbolic representations of the constraints which have to be met by an implementation to be feasible. SAT-decoding has been used for the design space exploration for single-mode systems, thus, we significantly enhance it as our approach will be able to cope with multimode systems as well as partial reconfiguration.…”

Section: Introductionmentioning

confidence: 99%

Symbolic design space exploration for multi-mode reconfigurable systems

Wildermann

Reimann

Ziener

et al. 2011

Proceedings of the Seventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis

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Abstract-In today's complex embedded systems not all applications are running all the time, but depend on the operational mode. By incorporating knowledge about the temporal behavior of such multi-mode systems, it is possible to share hardware by means of partial reconfiguration, and thus, reduce costs and improve performance. In this paper, we specify the temporal behavior of the functionality by applying known models based on state machines. In addition, we introduce an architectural model that allows to express the characteristics of nowadays partially reconfigurable architectures, focusing on FPGAs. We develop a symbolic encoding of this novel system specification, which allows to perform a unified system synthesis for allocation, binding, placement of partially reconfigurable modules, and routing the on-chip communication. The proposed encoding enables the use of sophisticated optimization techniques, coupling a SAT solver with a Multi-objective Evolutionary Algorithm. The proposed methodology is highly applicable for building multi-mode systems on advanced reconfigurable technology. We demonstrate this by experiments on test-cases from the image processing domain applying state-of-the-art technology. The results show the superiority of the presented approach in terms of run-time and quality of the found solutions compared to existing system synthesis approaches.

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“…The priority ρ i of a variable x i is randomly chosen in R [0,1] (line 7). For each variable x i a mutation is done with the probability r. The mutation increases the priority ρ i by 1 and flips the prioritized phase value σ i (line [8][9][10][11].…”

Section: Algorithm 2 Feasibility-preserving Crossover and Mutation Opmentioning

confidence: 99%

“…By using a Pseudo-Boolean (PB) solver [7] as a decoder, this method always obtains feasible solutions by mapping from a bounded search space to feasible solutions. In combination with an Evolutionary Algorithm (EA), a good convergence towards the optimal solutions also on large and complex real-world problems [8] is reached.…”

mentioning

confidence: 99%

A Feasibility-Preserving Crossover and Mutation Operator for Constrained Combinatorial Problems

Lukasiewycz

Glaß

Teich

2008

Parallel Problem Solving From Nature – PPSN X

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Abstract. This paper presents a feasibility-preserving crossover and mutation operator for evolutionary algorithms for constrained combinatorial problems. This novel operator is driven by an adapted Pseudo-Boolean solver that guarantees feasible offspring solutions. Hence, this allows the evolutionary algorithm to focus on the optimization of the objectives instead of searching for feasible solutions. Based on a proposed scalable testsuite, six specific testcases are introduced that allow a sound comparison of the feasibility-preserving operator to known methods. The experimental results show that the introduced approach is superior to common methods and competitive to a recent state-of-the-art decoding technique.

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Efficient symbolic multi-objective design space exploration

Cited by 52 publications

References 18 publications

Concurrent topology and routing optimization in automotive network integration

Concurrent topology and routing optimization in automotive network integration

Symbolic design space exploration for multi-mode reconfigurable systems

A Feasibility-Preserving Crossover and Mutation Operator for Constrained Combinatorial Problems

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