An exact algorithm for wirelength optimal placements in VLSI design

Funke, Julia; Hougardy, Stefan; Schneider, Ján

doi:10.1016/j.vlsi.2015.07.001

Cited by 25 publications

(9 citation statements)

References 39 publications

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“…1, we can see that BO performs competitively with only 520 evaluations of the objective on apte which is the smallest problem. We acknowledge that on the designs with a larger number of macros, ami33 and ami49, there is a non-negligible gap between BO and eWL (Funke et al, 2016). However, we expect that this gap does not translate to the real world applications that we envision since eWL cannot optimize macro placements with standard cells, while BO and SA can.…”

Section: Methodsmentioning

confidence: 97%

Bayesian Optimization for Macro Placement

Oh¹,

Bondesan²,

Kianfar³

et al. 2022

Preprint

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Section: Methodsmentioning

confidence: 97%

Bayesian Optimization for Macro Placement

Oh¹,

Bondesan²,

Kianfar³

et al. 2022

Preprint

View full text Add to dashboard Cite

“…Because of element size and signal delay reduction, over 80% of the total time delay now corresponds to interconnection delays. In this context, placement is becoming increasingly important, and new methods are required [14,15].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

et al. 2020

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This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies.The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N 2 ).

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“…In CFL problems, there is no limitation for the choice of locations, and facilities are represented by rectangles. CFLs have been used on various problems, such as finding layout of temporary buildings on a construction site (Kumar and Cheng, 2015) and designing hospitals (Hahn and Krarup, 2001) and developing microprocessor layouts (Funke et al , 2016). If material handlings are performed via halls, orthogonal linear distance is used.…”

Section: Related Literaturementioning

confidence: 99%

A new optimization model for design of traditional cockpit interfaces

Şenol

2020

AEAT

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Purpose Real flight is cognitively demanding; accordingly, both indicators and display panel layout should be user-friendly to improve pilot-aircraft interaction. Poor pilot-interface interactions in aircrafts could result in accidents. Although a general reason of accidents is improper displays, relatively few studies were conducted on interfaces. This study aims to present an optimization model to create intuitively integrated user-friendly cockpit interfaces. Design/methodology/approach Subjectivity within most usability evaluation techniques could bring about interface design problems. A priori information about indicator’s possible locations may be available or unavailable. Thus different analytical approaches must be applied for modifications and new interface designs. Relative layout design (RLD) model was developed and used in new interface designs to optimize locations of indicators. This model was based on layout optimization and constructed in accordance with design requirements, ergonomic considerations with the pilot preferences. RLD model optimizes interface design by deploying indicators to the best locations to improve usability of display panel, pilot-aircraft interaction and flight safety. Findings Optimum interfaces for two problem instances were gathered by RLD model in 15.77 CPU(s) with 10 indicators and 542.51 CPU(s) with 19 indicators. A comparison between relative and existing cockpit interfaces reveals that locations of six navigation and four mechanical system indicators are different. The differences may stem from pilots’ preferences and relativity constraints. Both interfaces are more similar for the central part of the display panel. The objective function value of relative interface design (Opt: 527938) is far better than existing interface (737100). The RLD model improved usability of existing interface (28.61 per cent considering decrease in the objective function values from 737100 to 527938. Practical implications Future cockpit and new helicopter interface designs may involve RLD model as an alternative interface design tool. Furthermore, other layout optimization problems, e.g. circuit boards, microchips and engines, etc. could be handled in a more realistic manner by RLD model. Originality/value Originality and impact of this study related to development and employment of a new optimization model (RLD) on cockpit interface design for the first time. Engineering requirements, human factors, ergonomics and pilots’ preferences are simultaneously considered in the RLD model. The subjectivity within usability evaluation techniques could be diminished in this way. The contributions of RLD model to classical facility layout models are relativity constraints with the physical constrictions and ergonomic objective function weights. Novelty of this paper is the development and employment of a new optimization model (RLD) to locate indicators.

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An exact algorithm for wirelength optimal placements in VLSI design

Cited by 25 publications

References 39 publications

Bayesian Optimization for Macro Placement

Bayesian Optimization for Macro Placement

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

A new optimization model for design of traditional cockpit interfaces

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