Partitioning of VLSI Circuits on Subcircuits with Minimal Number of Connections Using Evolutionary Algorithm

Słowik, Adam; Bialko, M.

doi:10.1007/11785231_50

Cited by 21 publications

(20 citation statements)

References 5 publications

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“…Graph partitioning problem is widely applied in task scheduling [1], very large scale integration design [2], hardware/software (HW/SW) co-design [3] etc. Various approximation algorithms have been proposed for solving graph partitioning problems as such problems are shown to be NPhard.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for Balanced Graph Bi-partitioning

Jiang

Zheng

et al. 2014

2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security

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Graph partitioning has been widely applied in cloud computing, data centers, virtual machine scheduling, hardware/software co-design, and VLSI circuit design, etc. The general graph partitioning problem is known to be NP-hard. This paper investigates how to partition the vertex set of an undirected weighted graph into two disjoint subsets, such that the total vertex-weights of the two subsets are nearly equal, and the total weight of the edges connecting the two subsets is minimized. A heuristic algorithm is proposed to initialize an approximate bipartition such that the total vertex-weight of each subset is close to that of the other. The proposed algorithm constructs a subset by selecting a group of neighboring vertices with the highest gain from the original graph for inclusion into the subset. A customized tabu search is proposed to further refine the initial partition, which minimizes the communication cost and keeps partition balanced. Experimental results show that the proposed algorithms outperform the state-of-the-art on the public benchmarks, with the improvement of up to 79% for certain cases.Index Terms-Graph partitioning, algorithm, heuristic, tabu search 978-1-4799-6123-8/14 $31.00

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Section: Introductionmentioning

confidence: 99%

Algorithms for Balanced Graph Bi-partitioning

Jiang

Zheng

et al. 2014

2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security

View full text Add to dashboard Cite

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“…The Graph Partitioning Problem (GPP) is one of the fundamental combinatorial optimization problems which is notable for its applicability to a wide range of domains, such as VLSI design [1,43], data mining [49], image segmentation [42], etc. Since the general GPP is NP-complete, approximate methods constitute a natural and useful approach to address this problem.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristics for the Graph Partitioning Problem

Benlic

Hao

2013

Hybrid Metaheuristics

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The Graph Partitioning Problem (GPP) is one of the most studied NPcomplete problems notable for its broad spectrum of applicability such as in VLSI design, data mining, image segmentation, etc. Due to its high computational complexity, a large number of approximate approaches have been reported in the literature. Hybrid algorithms that are based on adaptations of popular metaheuristic techniques have shown to provide outstanding performance in terms of partition quality. In particular, it is the hybrids between well-known metaheuristics and multilevel strategies that report partitions of the minimal cut-size value. However, metaheuristic hybrids generally require more computing time than those based on greedy heuristics which can generate partitions of acceptable quality in a matter of seconds even for very large graphs. This chapter is dedicated to a review on some representative hybrid metaheuristic approaches including genetic local search, basic multilevel search and recent development on hybrid multilevel search.

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“…Additional KLlike heuristics are reported, for instance, in [17] for bisection and in [8,18,28] for k-partitioning. Other well-known algorithms are based on popular metaheuristics such as tabu search [7,23,3], simulated annealing [15], genetic and evolutionary algorithms [27,5,20,25,26].…”

Section: Introdutionmentioning

confidence: 99%

“…The particular partitioning case when k is set to two is often called bisection. The class of partitioning problems is notable for its applicability to a wide range of important applications such as VLSI design [1,26], data mining [30], and image segmentation [24].…”

Section: Introdutionmentioning

confidence: 99%

An effective multilevel tabu search approach for balanced graph partitioning

Benlic

Hao

2011

Computers & Operations Research

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Graph partitioning is one of the fundamental NP-complete problems which is widely applied in many domains, such as VLSI design, image segmentation, data mining etc. Given a graph G = (V, E), the balanced k-partitioning problem consists in partitioning the vertex set V into k disjoint subsets of about the same size, such that the number of cutting edges is minimized. In this paper, we present a multilevel algorithm for balanced partition, which integrates a powerful refinement procedure based on tabu search with periodic perturbations. Experimental evaluations on a wide collection of benchmark graphs show that the proposed approach not only competes very favorably with the two well-known partitioning packages METIS and CHACO, but also improves more than two thirds of the best balanced partitions ever reported in the literature.Keywords: Balanced partitioning; multilevel approach; iterated tabu search. IntrodutionGiven an undirected graph G = (V, E), where V and E denote sets of vertices and edges respectively, the balanced k-partitioning problem consists in partitioning the vertex set V into k (k ≥ 2) disjoint subsets of approximately equal size, such that the number of cutting edges (i.e. edges whose endpoints belong to different subsets) is minimized. The particular partitioning case when k

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Partitioning of VLSI Circuits on Subcircuits with Minimal Number of Connections Using Evolutionary Algorithm

Cited by 21 publications

References 5 publications

Algorithms for Balanced Graph Bi-partitioning

Algorithms for Balanced Graph Bi-partitioning

Hybrid Metaheuristics for the Graph Partitioning Problem

An effective multilevel tabu search approach for balanced graph partitioning

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