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A Study on Unroutable Placement Recognition

A Study on Unroutable Placement Recognition. Wen- Hao Liu, Tzu-Kai Chien , and Ting-Chi Wang ISPD’14. Outline. Introduction Unrouted Region Algorithm Sliding W indow Layout S canning Window Dimension Determination LBTO Identification Experimental Results Conclusion.

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A Study on Unroutable Placement Recognition

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  1. A Study on UnroutablePlacement Recognition Wen-Hao Liu, Tzu-Kai Chien, and Ting-Chi Wang ISPD’14

  2. Outline • Introduction • Unrouted Region • Algorithm • Sliding Window Layout Scanning • Window Dimension Determination • LBTO Identification • Experimental Results • Conclusion

  3. Introduction • As technology advances, the routing problem become more difficult, two research directions are promoted : improve the capability of routing tools ,routability-driven placement • One may ask whether existing global routers are not powerful, or whether identifying an overflow-free routing result for these benchmarks is impossible.

  4. Introduction • This paper presents unroutable placement recognizer , which can recognize some placements that are exactly unroutable. • Also, if a placement is recognized to be unroutable, the recognizer can report a lower bound of total overflow (LBTO) for the given placement.

  5. UnroutedRegion • If |S(Rx,y,w,h)|>c(Rx,y,w,h), Rx,y,w,his defined to be an unroutable region.

  6. Layout Scanning • The layout scanning algorithm uses a dynamic • programming (DP) technique to construct S(Rx,y,w,h) efficiently based on the following property.

  7. Layout Scanning

  8. Layout Scanning

  9. Layout Scanning

  10. Window Dimension Determination • Different widths and heights of a sliding window would affect the recognition rate.

  11. Window Dimension Determination Sampling stage:

  12. Window Dimension Determination • Second stage :

  13. LBTO Identification • Identifying the minimum total overflow for a layout is a hard problem and has never been studied. This work uses a heuristic method to identify a LBTO

  14. LBTO Identification Bridge shared by more than one region

  15. LBTO Identification In this work, we reduce the LBTO identification problem to a maximum weight independent set (MWIS) problem. We adopt greedy algorithm to solve it.

  16. Experimental Results • The proposed algorithms are implemented in C/C++/openMPand tested on a 2.4GHz Intel Xeon-based Linux server with 96GB memory. • We select a set of hard-to-route placements from two benchmark suites. • The first benchmark suite is from ISPD08 global routing contest : newblue3 , newblue4, newblue7 and bigblue7

  17. Experimental Results

  18. Experimental Results

  19. Experimental Results

  20. Experimental Results

  21. Conclusion • The proposed unroutable placement recognizer can confirm that a placement is exactly unroutable. • In addition, the recognizer can report a lower bound of total overflow for an unroutableplacement.

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