IEICE TRANSACTIONS on Fundamentals
Efficient Large Scale Integration Power/Ground Network Optimization Based on Grid Genetic Algorithm
Summary :
In this paper we propose a novel and efficient method for the optimization of the power/ground (P/G) network in VLSI circuit layouts with reliability constraints. Previous algorithms in the P/G network sizing used the sequence-of-linear-programming (SLP) algorithm to solve the nonlinear optimization problems. However the transformation from nonlinear network to linear subnetwork is not optimal enough. Our new method is inspired by the biological evolution and use the grid-genetic-algorithm (GGA) to solve the optimization problem. Experimental results show that new P/G network sizes are smaller than previous algorithms, as the fittest survival in the nature. Another significant advance is that GGA method can be applied for all P/G network problems because it can get the results directly no matter whether these problems are linear or not. Thus GGA can be adopted in the transient behavior of the P/G network sizing in the future, which recently faces on the obstacles in the solution of the complex nonlinear problems.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.12 pp.3412-3420
- Publication Date
- 2005/12/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietfec/e88-a.12.3412
- Type of Manuscript
- Special Section PAPER (Special Section on VLSI Design and CAD Algorithms)
- Category
- Power/Ground Network
Authors
Keyword
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Yun YANG, Atsushi KUROKAWA, Yasuaki INOUE, Wenqing ZHAO, "Efficient Large Scale Integration Power/Ground Network Optimization Based on Grid Genetic Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E88-A, no. 12, pp. 3412-3420, December 2005, doi: 10.1093/ietfec/e88-a.12.3412.
Abstract: In this paper we propose a novel and efficient method for the optimization of the power/ground (P/G) network in VLSI circuit layouts with reliability constraints. Previous algorithms in the P/G network sizing used the sequence-of-linear-programming (SLP) algorithm to solve the nonlinear optimization problems. However the transformation from nonlinear network to linear subnetwork is not optimal enough. Our new method is inspired by the biological evolution and use the grid-genetic-algorithm (GGA) to solve the optimization problem. Experimental results show that new P/G network sizes are smaller than previous algorithms, as the fittest survival in the nature. Another significant advance is that GGA method can be applied for all P/G network problems because it can get the results directly no matter whether these problems are linear or not. Thus GGA can be adopted in the transient behavior of the P/G network sizing in the future, which recently faces on the obstacles in the solution of the complex nonlinear problems.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.12.3412/_p
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@ARTICLE{e88-a_12_3412,
author={Yun YANG, Atsushi KUROKAWA, Yasuaki INOUE, Wenqing ZHAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Efficient Large Scale Integration Power/Ground Network Optimization Based on Grid Genetic Algorithm},
year={2005},
volume={E88-A},
number={12},
pages={3412-3420},
abstract={In this paper we propose a novel and efficient method for the optimization of the power/ground (P/G) network in VLSI circuit layouts with reliability constraints. Previous algorithms in the P/G network sizing used the sequence-of-linear-programming (SLP) algorithm to solve the nonlinear optimization problems. However the transformation from nonlinear network to linear subnetwork is not optimal enough. Our new method is inspired by the biological evolution and use the grid-genetic-algorithm (GGA) to solve the optimization problem. Experimental results show that new P/G network sizes are smaller than previous algorithms, as the fittest survival in the nature. Another significant advance is that GGA method can be applied for all P/G network problems because it can get the results directly no matter whether these problems are linear or not. Thus GGA can be adopted in the transient behavior of the P/G network sizing in the future, which recently faces on the obstacles in the solution of the complex nonlinear problems.},
keywords={},
doi={10.1093/ietfec/e88-a.12.3412},
ISSN={},
month={December},}
Copy
TY - JOUR
TI - Efficient Large Scale Integration Power/Ground Network Optimization Based on Grid Genetic Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3412
EP - 3420
AU - Yun YANG
AU - Atsushi KUROKAWA
AU - Yasuaki INOUE
AU - Wenqing ZHAO
PY - 2005
DO - 10.1093/ietfec/e88-a.12.3412
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2005
AB - In this paper we propose a novel and efficient method for the optimization of the power/ground (P/G) network in VLSI circuit layouts with reliability constraints. Previous algorithms in the P/G network sizing used the sequence-of-linear-programming (SLP) algorithm to solve the nonlinear optimization problems. However the transformation from nonlinear network to linear subnetwork is not optimal enough. Our new method is inspired by the biological evolution and use the grid-genetic-algorithm (GGA) to solve the optimization problem. Experimental results show that new P/G network sizes are smaller than previous algorithms, as the fittest survival in the nature. Another significant advance is that GGA method can be applied for all P/G network problems because it can get the results directly no matter whether these problems are linear or not. Thus GGA can be adopted in the transient behavior of the P/G network sizing in the future, which recently faces on the obstacles in the solution of the complex nonlinear problems.
ER -
English
