Solution Space Reduction of Simulated Evolution Algorithm for Solving Standard Cell Placement Problem

Author(s):  
Yoichi Shiraishi ◽  
Takaaki Ono ◽  
Mona Abo El Dahb
2003 ◽  
Vol 44 (2) ◽  
pp. 227-247 ◽  
Author(s):  
Habib Youssef ◽  
Sadiq M Sait ◽  
Hussain Ali

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Najwa Altwaijry ◽  
Mohamed El Bachir Menai

The standard cell placement (SCP) problem is a well-studied placement problem, as it is an important step in the VLSI design process. In SCP, cells are placed on chip to optimize some objectives, such as wirelength or area. The SCP problem is solved using mainly four basic methods: simulated annealing, quadratic placement, min-cut placement, and force-directed placement. These methods are adequate for small chip sizes. Nowadays, chip sizes are very large, and hence, hybrid methods are employed to solve the SCP problem instead of the original methods by themselves. This paper presents a new hybrid method for the SCP problem using a swarm intelligence-based (SI) method, called SwarmRW (swarm random walk), on top of a min-cut based partitioner. The resulting placer, called sPL (swarm placer), was tested on the PEKU benchmark suite and compared with several related placers. The obtained results demonstrate the effectiveness of the proposed approach and show that sPL can achieve competitive performance.


1998 ◽  
Vol 07 (04) ◽  
pp. 443-451 ◽  
Author(s):  
ASHUTOSH SAXENA ◽  
SUJU M. GEORGE ◽  
P. RAMBABU

Neural Network is used as a tool for estimating interconnection wire-length in VLSI standard cell placement problem. Conventional methods for estimating the interconnection wire-length viz., Bounding Rectangle method, provide inaccurate estimate of the interconnection wire-length and does not depict the interconnection procedure in a layout and separates routing and placement tasks distinctly. The proposed mechanism utilizes the neural network characteristics in understanding the functional mapping between input and output, to estimate the interconnection wire-length. Experiments were performed for different number of cells with varying complexity of interconnections. In all the cases, the performance of the Neural Network is found to be superior to the results obtained using Bounding Rectangle procedure.


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