Quadratic placement revisited

  • Authors:
  • C. J. Alpert;T. Chan;D. J.-H. Huang;I. Markov;K. Yan

  • Affiliations:
  • IBM Austin Research Laboratory, Austin, TX;UCLA Mathematics Dept., Los Angeles, CA;UCLA Computer Science Dept., Los Angeles, CA;UCLA Mathematics Dept., Los Angeles, CA;UCLA Computer Science Dept., Los Angeles, CA

  • Venue:
  • DAC '97 Proceedings of the 34th annual Design Automation Conference
  • Year:
  • 1997

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Abstract

The "quadratic placement" methodology is rooted in [Module Placement Based on Resistive Network Optimization, Proud: A Sea-Of-Gate Placement Algorithm, A Combined Force and Cut Algorithm for Hierarchical VLSI Layout]and is reputedly used in many commercial and in-house tools forplacement of standard-cell and gate-array designs. The methodologyiterates between two basic steps: solving sparse systems oflinear equations, and repartitioning. This work dissects the implementationand motivations for quadratic placement. We firstshow that (i) Krylov subspace engines for solving sparse systemsof linear equations are more effective than the traditional successiveover-relaxation (SOR) engine [A Unified Approach to Partitioning and Placement] and (ii) order convergence criteriacan maintain solution quality while using substantially fewersolver iterations. We then discuss the motivations and relevanceof the quadratic placement approach, in the context of past and futurealgorithmic technology, performance requirements, and designmethodology. We provide evidence that the use of numerical linearsystems solvers with quadratic wirelength objective may be due tothe pre-1990's weakness of min-cut partitioners, i.e., numerical engineswere needed to provide helpful hints to min-cut partitioners.Finally, we note emerging methodology drivers in deep-submicrondesign that may require new placement approaches to the placement problem.