VLSI Floorplanning Using Entropy Based Intelligent Genetic Algorithm

Singh, Amarbir; Jain, Leena

doi:10.1007/978-981-13-0755-3_5

Cited by 4 publications

(6 citation statements)

References 28 publications

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“…Compared with [32], the proposed algorithm uses nonstandard genetic operators, which are based on a knowledge of the specific problem to be solved. This helps avoid illegal solutions in the interim placement stage.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Singh and Jain [32] proposed an intellectual approach for VLSI placement, which was based on a heuristic placement strategy and their entropy-based intelligent genetic algorithm (EBIGA). The concept of entropy was introduced in the GA to resolve the problem of finding local optima.…”

Section: Genetic Algorithms For Vlsi Placementmentioning

confidence: 99%

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

et al. 2020

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“…In recent years, GA has emerged as a powerful and practical method in various fields of search and optimization. It is used in a variety of areas such as Music Production [32], Very Large Scale Integration (VLSI) [33], Machine Learning [34], and Image Processing [35]. GA is a subset of evolutionary algorithms that uses techniques inspired from biology such as inheritance, mutation, selection and crossover.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Enhancing the Acceleration and Accuracy of Infrasound Signal Detection and Estimation Based on Fisher Ratio Using Genetic Algorithm

Masoomi¹,

Sadeghi²,

khadem³

2023

Preprint

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<p>‎Atmospheric sound waves with frequencies below the human hearing threshold in the range of 0.002 Hz to 20 Hz are generally referred to as infrasound‎. ‎Wind is the main noise in the above-mentioned frequency range‎. ‎The operation of receiving and detecting infrasound are often hampered by wind‎. ‎Therefore‎, ‎high quality detectors are required‎. ‎For this purpose‎, ‎sensor arrays and array signal processing techniques are utilized‎. ‎Fisher ratio-based signal detection is a widely used and powerful method in the field of infrasound‎. ‎The main drawback of this approach is its high computational time due to the repeated computation of test statistics for each element of the slowness grid‎. ‎Thus‎, ‎the researchers use a relatively low-resolution slowness grid in order to save time in processing‎. ‎On the other hand‎, ‎low resolution grid results in an error in the values of estimated parameters of infrasound wave‎. ‎In this study‎, ‎a genetic algorithm based detection method is proposed in order to overcome the fundamental problems of the Fisher method‎. ‎In the proposed method‎, ‎the slowness grid components (px‎,‎py) are defined as the chromosome for the genetic algorithm‎. ‎Despite the previous methods‎, ‎the genetic algorithm has created the advantage that searching could be conducted in a continuous slowness grid‎. ‎Therefore‎, ‎the continuity of the grid and searching only a limited number of slowness vectors reduce error rates and processing time respectively‎. ‎The error of apparent velocity and incoming orientation became 0.5923 and 0.0710 respectively‎, ‎and the processing time decreased considerably from 25835.07 seconds to 533.55 seconds on average.</p>

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“…B*tree representations with Genetic (GA) algorithm is used to calculate floorplanning temperature based on the power dissipation. Amarbir Singh, Leena Jain (2016) [27] proposed an algorithm for the modern VLSI Floorplanning problem with outline-free and hard modules constraints. This algorithm is based on the genetic algorithm and integer coding representation.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Different Methodologies to Solve Vlsi Floorplanning Problem

Jain,

Singh

2022

YMER

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Due to the exponential increase in number of components on a VLSI (Very Large-Scale Integration) chip over the years, there is a need to develop automated algorithms to decide the relative positions of circuits on a chip. In order to improve the performance of a chip, it is essential to deal with multiple objectives including area and wire length during the floor planning phase. Modern very large-scale integration technology is based on fixed-outline floorplan constraints, generally with an objective of minimizing area and wirelength between the modules. This survey paper gives an up-to-date account on various approaches used to solve VLSI floor planning problem. Keywords—Genetic algorithm, Non-slicing floorplan, Soft modules, VLSI floor planning.

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VLSI Floorplanning Using Entropy Based Intelligent Genetic Algorithm

Cited by 4 publications

References 28 publications

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

Enhancing the Acceleration and Accuracy of Infrasound Signal Detection and Estimation Based on Fisher Ratio Using Genetic Algorithm

Different Methodologies to Solve Vlsi Floorplanning Problem

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