Abstract

In this paper, a new optimization technique called SOFT(self-organizing fuzzy technique) is proposed to solve the macro‐cell placement problem. In SOFT, different criteria are simultaneously accounted by a novel fuzzy gain function which models expert knowledge to control the optimization process. The presented procedure is an adaptation of Kohonen′s self-organization algorithm which is well suited for implementation on massively parallel architecture for fast computing. The MCNC benchmark examples are presented to verify the performance and feasibility of SOFT. Comparisons are made with the Hopfield network, SOAP and TimberWolf MC5.6. Experiments show that the proposed method yields an average of 17% improvement in total wire length compared with previous methods. Large size problems with 225 and 1024 arbitrarily‐sized macrocells are also presented.

Highlights

  • Macro-cell placement which packs arbitrarilysized circuit blocks into a given layout region is a very important step in VLSI custom-chip design, since it has a pronounced effect on the final circuit layout

  • We model the module overlap cost function heuristically using the distance between cells to make the computations simpler

  • Several macro-cell placement examples (i.e., MCNC benchmark circuits: Xerox and Ami33) were presented to our solution procedure to prove that our algorithm can handle these problems

Read more

Summary

Introduction

Macro-cell placement which packs arbitrarilysized circuit blocks into a given layout region is a very important step in VLSI custom-chip design, since it has a pronounced effect on the final circuit layout. The branch and bound method which seeks a solution by tracing a logical tree structure is able to guarantee optimum results, but the run time is excessive for reasonably sized problems. The genetic-based method derived from biological phenomena is ineffective unless a clever representation scheme is devised to represent the physical placement as a genetic code Force-directed methods and analytical methods [20,21] are iterative approaches that accept the configuration only if the value of the objective function is reduced They are characterized by their inherently greedy nature for fast convergence. The disadvantages of these methods are that they do not permit changes to previous decisions and they get trapped in local optima

Objectives
Results
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call