RQL: global placement via relaxed quadratic spreading and linearization

  • Authors:
  • Natarajan Viswanathan;Gi-Joon Nam;Charles J. Alpert;Paul Villarrubia;Haoxing Ren;Chris Chu

  • Affiliations:
  • IBM Corporation, Austin, TX and Iowa State University, Ames, IA;IBM Corporation, Austin, TX;IBM Corporation, Austin, TX;IBM Corporation, Austin, TX;IBM Corporation, Austin, TX;Iowa State University, Ames, IA

  • Venue:
  • Proceedings of the 44th annual Design Automation Conference
  • Year:
  • 2007

Quantified Score

Hi-index 0.02

Visualization

Abstract

This paper describes a simple and effective quadratic placement algorithm called RQL. We show that a good quadratic placement, followed by local wirelength-driven spreading can produce excellent results on large-scale industrial ASIC designs. As opposed to the current top performing academic placers [4, 7, 11], RQL does not embed a linearization technique within the solver. Instead, it only requires a simpler, pure quadratic objective function in the spirit of [8, 10, 23]. Experimental results show that RQL outperforms all available academic placers on the ISPD-2005 placement contest benchmarks. In particular, RQL obtains an average wire-length improvement of 2.8%, 3.2%, 5.4%, 8.5%, and 14.6% versus mPL6 [5], NTUPlace3 [7], Kraftwerk [20], APlace2.0 [11], and Capo10.2 [18], respectively. In addition, RQL is three, seven, and ten times faster than mpL6, Capo10.2, and APlace2.0, respectively. On the ISPD-2006 placement contest benchmarks, on average, RQL obtains the best scaled wirelength among all available academic placers.