NDLI: A concurrent approach to clustering algorithm with applications to VLSI domain

Content Provider	IEEE Xplore Digital Library
Author	Iqbal, S.M.A. Monir, M.I. Sayeed, T. Misbah-Uddin, A.H.M.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Premier Univ., Chittagong (Iqbal, S.M.A.; Monir, M.I.; Sayeed, T.; Misbah-Uddin, A.H.M.)
Abstract	Circuit partitioning plays an important role in physical design automation of very large scale integration (VLSI) chips. In this brief we present a new connectivity based top down as well as bottom up approach to clustering algorithm for VLSI circuit partitioning. The proposed clustering algorithm partitions the circuit by focusing on highly interconnected cell groups. This clustering algorithm leads to a parallel implementation in which multiple processors are used to identify clusters simultaneously. The process starts with forming clusters by grouping the cells that are tightly connected and as well as the cells that are loosely connected. Considering both types of groups has the advantage that clusters formed from this technique will be highly connected and compact too. Therefore the proposed clustering method can reduce the size and also speed-up the large-scale partitioning problem without loosing partitioning solution qualities. The performance of the proposed clustering algorithm is evaluated on a standard set of partitioning benchmark-ISPD98 benchmark suite.
Starting Page	476
Ending Page	480
File Size	1031083
Page Count	5
File Format	PDF
ISBN	9781424421350
DOI	10.1109/ICCITECHN.2008.4802982
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-24
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	cluster Clustering methods Circuits Very large scale integration threshold Partitioning algorithms Application software Information technology Base Concurrent computing Computer science partition Clustering algorithms push-pop Iterative algorithms
Content Type	Text
Resource Type	Article

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