Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm

Paramasivam, Sivaranjani; Athappan, Senthilkumar; Natrajan, Eswari Devi; Shanmugam, M.

doi:10.4236/cs.2016.75048

Cited by 8 publications

(3 citation statements)

References 23 publications

(26 reference statements)

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“…To examine the effect of EMF radiations on the morphology of the D. Melanogaster brain, four classifiers were used to segregate it. These four classifiers were naïve Bayes [15][16][17], artificial neural network [18][19][20][21][22], support vector machine [23][24][25], and unsystematic forest.…”

Section: Segregating Open/not Open Groups Using Machine Learningmentioning

confidence: 99%

Machine Learning Technique to Detect Radiations in the Brain

Gothai¹,

Baseera²,

Prabu³

et al. 2022

Computer Systems Science and Engineering

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The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due to the revelation of EMF.Here, Drosophila melanogaster acts as a specimen under the revelation of EMF. Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF. The geometrical characteristics of the brain image of that is microscopic segmented are analyzed. Analysis results reveal the occurrence of several prejudicial characteristics that can be processed by machine learning techniques. The important prejudicial characteristics are given to four varieties of classifiers such as naïve Bayes, artificial neural network, support vector machine, and unsystematic forest for the classification of open or nonopen microscopic image of D. melanogaster brain. The results are attained through various experimental evaluations, and the said classifiers perform well by achieving 96.44% using the prejudicial characteristics chosen by the feature selection method. The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity, where the machine learning techniques produce an effective framework for image processing.

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Section: Segregating Open/not Open Groups Using Machine Learningmentioning

confidence: 99%

Machine Learning Technique to Detect Radiations in the Brain

Gothai¹,

Baseera²,

Prabu³

et al. 2022

Computer Systems Science and Engineering

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“…For SRAM designs in aerospace applications, the soft error rate, power consumption, size, and delay are the most critical metrics to consider. In addition, SRAM cells take up much space in the device on the chip [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Low-Power SRAM Cell and Array Structure in Aerospace Applications: Single-Event Upset Impact Analysis

Gavaskar¹,

Sivaranjani²,

Elango³

et al. 2022

Wireless Pers Commun

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One of the most critical functions of a computer is memory, as it would be impossible for it to function e ciently without it. The main memory of a computer is known as Random Access Memory (RAM). It stores operating system software, applications, and other data for the central processor unit (CPU) to perform operations swiftly and e ciently. Unfortunately, traditional static RAM (SRAMs) in aerospace applications suffers from high soft error issues such as Single Event Upset (SEU). To overcome the soft error problems, many Radiation-Hardened-Based Designs (RHBD) and Radiation-Hardened-Polar Designs (RHPD) have been developed, such as 12T We-Quatro, twelve-transistor (12T) Dice SRAM cells, and so on. However, they consume more total and static power, as well as have more delay and area. In this article, an RHPD and RHBD 12T SRAM cell is proposed to reduce power dissipation and area overhead. Compared to RHPD, the RHBD 12T SRAM cell devours less total and static power, and RHPD cells have less delay. The proposed SRAM cell is implemented in the 32 x 32 array architecture. The power consumption of a 32 x 32 SRAM array with a 12T RHBD SRAM cell is 1.33mW, which is 10.1% less than a 32 x 32 SRAM array with a 12T RHPD SRAM array is 4.23mW. Cadence virtuoso 6.1.5 at 45 nm Generic Process Design Kit (GPDK) technology le is used to simulate the comparative analysis for the SRAM cell.

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“…Venkatraman and Sundhararajan (2017) describe PSO using polish expression to fix modules with fixed outline constraint and a hybrid Ant colony optimization technique which can reduce the calculation time and can produce the enhanced floor-plan arrangement, it mainly concentrates on giving more suitable floor-plan in less time. Paramasivam S et al, (2016) propose a hybrid method using PSO and harmony Search (hS) to achieve global optima and local optima. The results obtained show an optimal solution for larger number of modules.…”

mentioning

confidence: 99%

VLSI Floorplan Area Optimization using Swarm Intelligence

Desai¹

2020

Biosci. Biotech. Res. Comm

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Floor-planning is a very important stage in the VLSI Physical design process. It determines size, performance, reliability and yield of the VLSI chips. VLSI floor-planning is considered as NP-hard in computational point of view. The modern VLSI technology is associated with fixed outline floor-plan constraint and the objective is to minimize the area and wire-length between the modules. In this paper Particle Swarm Optimization (PSO) algorithm is proposed. PSO is used to optimize the floor-plan area and to represent the floor-plan for non-slicing structure, where area is the physical quantity that is considered for optimization. PSO is an effective swarm intelligence search method which explores the search space during the optimization process to find a near optimal solution. The proposed PSO algorithm is tested with the Microelectronics Centre of North Carolina benchmark circuits (MCNC). The obtained results show that the proposed PSO has better optimization of area of floor-plan with optimal run-time compared to other existing optimization schemes. An area improvement of 7.8% and 11.9% is obtained with MCNC benchmarks ami33 and XEROX10 as compared to the existing methods.

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Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm

Cited by 8 publications

References 23 publications

Machine Learning Technique to Detect Radiations in the Brain

Machine Learning Technique to Detect Radiations in the Brain

Low-Power SRAM Cell and Array Structure in Aerospace Applications: Single-Event Upset Impact Analysis

VLSI Floorplan Area Optimization using Swarm Intelligence

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