Iterative heuristics for multiobjective VLSI standard cell placement

We employ two iterative heuristics for the optimization of VLSI standard cell placement. These heuristics are based on genetic algorithms (GA) and tabu search (TS) respectively. We address a multiobjective version of the problem, in which power dissipation, timing performance, and interconnect wire length are optimized while layout width is taken as a constraint. Fuzzy rules are incorporated in order to design a multiobjective cost function that integrates the costs of three objectives in a single overall cost value. A series of experiments is performed to study the effect of important algorithmic parameters of GA and TS. Both the techniques are applied to ISCAS-85/89 benchmark circuits and experimental results are reported and compared.