General iterative heuristics for VLSI multiobjective partitioning

Sadiq M. Sait; Aiman H. El-Maleh; Raslan H. Al-Abaji

General iterative heuristics for VLSI multiobjective partitioning

Sadiq M. Sait^*, Aiman H. El-Maleh, Raslan H. Al-Abaji

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

11 Scopus citations

Abstract

The problem of partitioning appears in several areas ranging from VLSI, parallel programming, to molecular biology. The interest in finding an optimal partition especially in VLSI has been a hot issue in recent years. In VLSI circuit partitioning, the problem of obtaining a minimum cut is of prime importance. With current trends, partitioning with multiple objectives which includes power, delay and area, in addition to minimum cut is in vogue. In this paper, we engineer two iterative heuristics for the optimization of VLSI netlist bi-Partitioning. These heuristics are based on Genetic Algorithms (GAs) and Tabu Search (TS) and incorporate fuzzy rules in order to handle the multiobjective cost function. Both heuristics are applied to ISCAS-85/89 benchmark circuits and experimental results are reported and compared.

Original language	English
Pages (from-to)	V497-V500
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	5
State	Published - 2003

ASJC Scopus subject areas

Electrical and Electronic Engineering

Cite this

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title = "General iterative heuristics for VLSI multiobjective partitioning",

abstract = "The problem of partitioning appears in several areas ranging from VLSI, parallel programming, to molecular biology. The interest in finding an optimal partition especially in VLSI has been a hot issue in recent years. In VLSI circuit partitioning, the problem of obtaining a minimum cut is of prime importance. With current trends, partitioning with multiple objectives which includes power, delay and area, in addition to minimum cut is in vogue. In this paper, we engineer two iterative heuristics for the optimization of VLSI netlist bi-Partitioning. These heuristics are based on Genetic Algorithms (GAs) and Tabu Search (TS) and incorporate fuzzy rules in order to handle the multiobjective cost function. Both heuristics are applied to ISCAS-85/89 benchmark circuits and experimental results are reported and compared.",

author = "Sait, \{Sadiq M.\} and El-Maleh, \{Aiman H.\} and Al-Abaji, \{Raslan H.\}",

year = "2003",

language = "English",

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pages = "V497--V500",

journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

issn = "0271-4310",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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TY - JOUR

T1 - General iterative heuristics for VLSI multiobjective partitioning

AU - Sait, Sadiq M.

AU - El-Maleh, Aiman H.

AU - Al-Abaji, Raslan H.

PY - 2003

Y1 - 2003

N2 - The problem of partitioning appears in several areas ranging from VLSI, parallel programming, to molecular biology. The interest in finding an optimal partition especially in VLSI has been a hot issue in recent years. In VLSI circuit partitioning, the problem of obtaining a minimum cut is of prime importance. With current trends, partitioning with multiple objectives which includes power, delay and area, in addition to minimum cut is in vogue. In this paper, we engineer two iterative heuristics for the optimization of VLSI netlist bi-Partitioning. These heuristics are based on Genetic Algorithms (GAs) and Tabu Search (TS) and incorporate fuzzy rules in order to handle the multiobjective cost function. Both heuristics are applied to ISCAS-85/89 benchmark circuits and experimental results are reported and compared.

AB - The problem of partitioning appears in several areas ranging from VLSI, parallel programming, to molecular biology. The interest in finding an optimal partition especially in VLSI has been a hot issue in recent years. In VLSI circuit partitioning, the problem of obtaining a minimum cut is of prime importance. With current trends, partitioning with multiple objectives which includes power, delay and area, in addition to minimum cut is in vogue. In this paper, we engineer two iterative heuristics for the optimization of VLSI netlist bi-Partitioning. These heuristics are based on Genetic Algorithms (GAs) and Tabu Search (TS) and incorporate fuzzy rules in order to handle the multiobjective cost function. Both heuristics are applied to ISCAS-85/89 benchmark circuits and experimental results are reported and compared.

UR - https://www.scopus.com/pages/publications/0038420032

M3 - Conference article

AN - SCOPUS:0038420032

SN - 0271-4310

VL - 5

SP - V497-V500

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

ER -

General iterative heuristics for VLSI multiobjective partitioning

Abstract

ASJC Scopus subject areas

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