Heuristic Approach to Evaluate the Performance of Optimization Algorithms in VLSI Floor Planning for ASIC Design

2019 ◽

Vol 8 (6) ◽

pp. 2604-2607

Keyword(s):

Physical Design ◽

Design Flow ◽

Prototype Model ◽

Asic Design ◽

Model Based ◽

Initial Stage ◽

Floor Planning ◽

Run Time ◽

Prototype Design

Nowadays in VLSI number of transistors integrated on a single silicon chip is increasing day by day and the complexity of the design is increases tremendously. This makes very difficult for the designer and EDA tools. As number of instances increases the run time and memory for implementing the design increases. This will make more pressure on the designer because if product is not completed within the time to market company will lost so much of money. Floorplanning is the basic building step for any hierarchical physical design flow. Floorplanning is taking more amount of time in entire design hierarchical flow. If floorplanning is not good the entire design will take more time and it will increase a greater number of iterations to complete the design. In the top-level chip planning the quality of the floorplanning depends on the proper alignment of blocks and easy to meet the timing and congestion. To reduce memory size of CPU and run time, in this project we are using a method of Backbox model based top level hierarchical floorplanning based physical design. The main aim of this project is to reduce the number of instances which are not necessary in the top level chip floor planning which reduces peak memory for the design and also reducing CPU run time for getting proper prototype design in the top level ASIC design and estimate the congestion in the design at initial stage and modify floor planning to obtain quality of prototype model in the floorplanning. This project is designed on cadence encounter tool.

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Fairness, LMX, and job performance: A fairness heuristic approach

PsycEXTRA Dataset ◽

10.1037/e518632013-129 ◽

2004 ◽

Author(s):

Jeff Johnson ◽

Talya N. Bauer ◽

Leslie B. Hammer ◽

Donald M. Truxillo

Keyword(s):

Job Performance ◽

Heuristic Approach

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Improve Linear Quadratic Regulator by Particle Swarm Optimization Algorithms for Two Wheeled Self Bala ncing Mobile robot

Iraqi Journal for Electrical And Electronic Engineering ◽

10.33762/eeej.2017.135280 ◽

2017 ◽

Vol 13 (2) ◽

pp. 173-179

Author(s):

A Noor.M.Mjeed ◽

A Ekhlas.H.Karam

Keyword(s):

Particle Swarm Optimization ◽

Mobile Robot ◽

Optimization Algorithms ◽

Particle Swarm ◽

Linear Quadratic Regulator ◽

Linear Quadratic ◽

Swarm Optimization

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Comparison of Meta-heuristic Algorithms on Benchmark Functions

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.41 ◽

2019 ◽

Vol 2 (3) ◽

pp. 508-517

Author(s):

FerdaNur Arıcı ◽

Ersin Kaya

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Differential Evolution Algorithm ◽

Population Based ◽

Time Interval ◽

Bee Colony ◽

Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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