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RQL: global placement via relaxed quadratic spreading and linearization
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 44th annual Design Automation Conference table of contents
San Diego, California
SESSION: Modern placement techniques table of contents
Pages: 453 - 458  
Year of Publication: 2007
ISBN ~ ISSN:0738-100X , 978-1-59593-627-1
Authors
Natarajan Viswanathan  IBM Corporation, Austin, TX and Iowa State University, Ames, IA
Gi-Joon Nam  IBM Corporation, Austin, TX
Charles J. Alpert  IBM Corporation, Austin, TX
Paul Villarrubia  IBM Corporation, Austin, TX
Haoxing Ren  IBM Corporation, Austin, TX
Chris Chu  Iowa State University, Ames, IA
Sponsors
: The EDA Consortium
: IEEE/CASS/CANDE/CEDA
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 5,   Downloads (12 Months): 26,   Citation Count: 2
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Natarajan Viswanathan: colleagues
Gi-Joon Nam: colleagues
Charles J. Alpert: colleagues
Paul Villarrubia: colleagues
Haoxing Ren: colleagues
Chris Chu: colleagues