Practical Problems in VLSI Physical Design Automation

Lim, Sung Kyu

doi:10.1007/978-1-4020-6627-6

Cited by 22 publications

(8 citation statements)

References 138 publications

(260 reference statements)

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“…In the genetic algorithm, new solutions are generate on the basis of various modified genetic operators, such as crossingover, mutation, reduction, etc. [9,10].…”

Section: Architecture Of Combined Search Methodsmentioning

confidence: 99%

Combined approach for VLSI placement problem

Kureichik¹,

Kureichik²,

Leschanov³

2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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-The work discusses a stage of electronic computing equipment automated design -VLSI fragments placement. This task belongs to the NP-complex class of problems. The paper presents the statement of the VLSI placement problem. It is proposed an "evolution" -"search" strategy. A new search architecture based on the proposed strategy is constructed. The principal difference of this approach is the division of the search process into two stages and the application of different methods on each of them. At the first search stage it is used a genetic algorithm. At the second search stage an annealing simulation algorithm is proposed, which allows to improve the result found by the genetic algorithm. Based on this approach, a combined algorithm has been developed. A computational experiment was carried out on test examples (benchmarks). The quality of the obtained placement is on average 3.5% higher than the placement results obtained by using the well-known Dragon 2.23 algorithm. Series of tests and experiments were carried out and showed the promise of this approach. Time complexity of the developed algorithm is represented as O (n 3 ).

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“…In the genetic algorithm, new solutions are generate on the basis of various modified genetic operators, such as crossingover, mutation, reduction, etc. [9,10].…”

Section: Architecture Of Combined Search Methodsmentioning

confidence: 99%

Combined approach for VLSI placement problem

Kureichik¹,

Kureichik²,

Leschanov³

2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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“…The developed a on the computer AMD FX(tm Processor 3.10 GHz, RAM 4,00Gb In order to determine feature algorithms some researches of quality for the various set (benchmarks) different in the elements [10][11][12] are made.…”

Section: Experimental Researchmentioning

confidence: 99%

Combined approach to place electronic computing equipment circuit elements

Kureichik

Zaruba

2015

2015 IEEE East-West Design &Amp; Test Symposium (EWDTS)

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The article considers one of the main problems of construction engineering that consists in the placement of electronic computing equipment (ECE) circuit elements in the limited area of construction. The placement of ECE circuit elements is a NP-hard and NP-full problem. In this regard, the purpose of investigation is a development of a combined approach to the ECE circuit elements placement based on the evolutionary modeling methods and fractals aggregation. There is a formulation of placement problem and a criterion of the total length of connections in the article. New search architecture based on the multilevel approach was offered. The procedure of compression of commutation circuit based on the usage of fractal aggregation mechanisms is offered at the first stage of search. At the second stage of search the genetic algorithm letting make effective permutation of circuit elements and get the sets of quasioptimal solutions during polynomial time is supposed to be used. An example of problem solving is described in the article. The software environment is created and the computing experiments are carried out on the test examples (benchmarks). The conducted tests and experiments show perspective of usage of this approach. At the best case time complexity of algorithm is represented by O(nlogn), at the worst it is represented by (n 2 ).

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“…Floorplanning neighborhood graph shows the neighboring relations between the bins of a floorplan. Each vertex of the neighborhood graph represents a bin of global placement and each edge between two vertices shows that their corresponding bins are neighbors [17].…”

Section: Problem Definitionmentioning

confidence: 99%

“…For each critical and near-critical net segment that has not been planned at the previous levels, the shortest path between its source and its sink is found by using Dijkstra algorithm [17]. Note that the shortest path is measured considering the length of various highway edges, the amount of resources that are available to each edge, and the congestion level of the bins.…”

Section: Highway Planning Algorithmmentioning

confidence: 99%