GDL

Journal from gdlhub / 2021-08-24 11:54:25
Oleh : S.B. Vinay Kumar, P.V. Rao, H.A. Sharath, B.M. Sachin, U.S Ravi, B.V. Monica, King Saud University
Dibuat : 2021-08-14, dengan 0 file

Keyword : VLSI design, Optimization, Bio-inspired algorithm, Self-adaptive PSO, Floor planning
Url : http://www.sciencedirect.com/science/article/pii/S1319157817302793
Sumber pengambilan dokumen : Web

Today, VLSI technology has taken a fundamental role in developing most of the high-tech electronic circuits. Even though VLSI design is renowned for its smaller size, lower cost, lower power, high reliability and high functionality, the design process takes long time and produces high risk. So, to obtain the knowledge regarding the different contributions on VLSI design, about 52 papers on the design of VLSI using optimization are reviewed here. Accordingly, VLSI design optimization is analyzed through different bio-inspired algorithms and the performance measures of different VLSI experimentations are compared. Further, various improvements on Self-Adaptive Particle Swarm Optimization (SA-PSO) and VLSI design optimization, without the adoption of bio-inspired algorithms, are examined. Additionally, the floor planning problem of VLSI is considered and reviewed. Eventually, this paper provides the diverse research gaps and challenges in VLSI design, which may be helpful for the authors and the philosophers to contribute for further research.

Deskripsi Alternatif :

Today, VLSI technology has taken a fundamental role in developing most of the high-tech electronic circuits. Even though VLSI design is renowned for its smaller size, lower cost, lower power, high reliability and high functionality, the design process takes long time and produces high risk. So, to obtain the knowledge regarding the different contributions on VLSI design, about 52 papers on the design of VLSI using optimization are reviewed here. Accordingly, VLSI design optimization is analyzed through different bio-inspired algorithms and the performance measures of different VLSI experimentations are compared. Further, various improvements on Self-Adaptive Particle Swarm Optimization (SA-PSO) and VLSI design optimization, without the adoption of bio-inspired algorithms, are examined. Additionally, the floor planning problem of VLSI is considered and reviewed. Eventually, this paper provides the diverse research gaps and challenges in VLSI design, which may be helpful for the authors and the philosophers to contribute for further research.

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