RQL: Global Placement via Relaxed Quadratic Spreading and Linearization

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 wirelength 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 am...

• Type: Conference
• Year: 2007
• Where: DAC
• Authors: Natarajan Viswanathan, Gi-Joon Nam, Charles J. Alpert, Paul Villarrubia, Haoxing Ren, Chris C. N. Chu
• Keywords: Algorithm Called Rql, Available Academic Placers, DAC 2007, Design Automation, Placement Contest Benchmarks