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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Viswanathan, N. Gi-Joon Nam Alpert, C.J. Villarrubia, P. Haoxing Ren Chu, C. |
| Copyright Year | 2007 |
| Description | Author affiliation: IBM Corp., Austin (Viswanathan, N.; Gi-Joon Nam; Alpert, C.J.; Villarrubia, P.; Haoxing Ren) |
| 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], NTUPlaceS [7], Kraflwerk [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. |
| Starting Page | 453 |
| Ending Page | 458 |
| File Size | 443724 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781595936271 |
| ISSN | 0738100X |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-06-04 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Association for Computing Machinery, Inc. (ACM) |
| Subject Keyword | Algorithm design and analysis Linearization techniques Partitioning algorithms Permission Large-scale systems Application specific integrated circuits Hardware Routing Testing Analytical models Force-vector Modulation Algorithms Design Analytical Placement |
| Content Type | Text |
| Resource Type | Article |
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