DMCA
ACCELERATING MULTIOBJECTIVE VLSI CELL PLACEMENT WITH PARALLEL EVOLUTIONARY/TABU SEARCH HEURISTICS
BibTeX
@MISC{Sait_acceleratingmultiobjective,
author = {Sadiq M. Sait and Mahmood R. Minhas and Mustafa I. Ali and Ali M. Zaidi},
title = {ACCELERATING MULTIOBJECTIVE VLSI CELL PLACEMENT WITH PARALLEL EVOLUTIONARY/TABU SEARCH HEURISTICS},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Multiobjective combinatorial optimization problems in various disciplines remain to be the focus of extensive research due to their inherent hard nature and difficulty of finding near-optimal solutions. Iterative heuristics like Tabu Search (TS) and Simulated Evolution (SimE) have successfully been employed to solve a range of such optimization problems [1]. These heuristics are able to obtain high quality solutions, but for most real-life large size problems they may have huge runtime requirements. Parallelization of these heuristics is one of the adopted practical approach to achieve the solutions within acceptable runtimes. In this paper we address a hard multiobjective optimization problem namely, VLSI cell placement [2] with three possibly conflicting objectives: interconnect wirelength, power dissipation, and timing performance. Two heuristics namely, parallel tabu search (TS) and parallel simulated evolution (SimE) are presented. Fuzzy rules are used to design a multiobjective aggregate cost function. The parallel TS implementation is a based on a synchronous candidate list partitioning model, whereas the parallel SimE implementation is based on random distribution of rows to processors [3, 4]. For comparison purposes, a parallel genetic algorithm (GA) based on the island model [5], and a parallel simulated annealing (SA) based on the asynchronous multiple-Markov chain [6] are also implemented. Results of experiments on ISCAS-85/89 benchmark circuits are presented, with solution quality and speedup used as metrics for the comparative/relative evaluation of the presented heuristics. 1.
Keyphrases
multiobjective combinatorial optimization problem asynchronous multiple-markov chain synchronous candidate list random distribution fuzzy rule inherent hard nature benchmark circuit comparative relative evaluation parallel t implementation iterative heuristic power dissipation parallel tabu search extensive research near-optimal solution solution quality island model vlsi cell placement various discipline optimization problem parallel genetic algorithm adopted practical approach parallel simulated annealing hard multiobjective optimization problem real-life large size problem presented heuristic multiobjective aggregate cost function huge runtime requirement parallel sime implementation high quality solution acceptable runtimes interconnect wirelength tabu search comparison purpose
