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Refined single trunk tree: a rectilinear steiner tree generator for interconnect prediction
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Source International Workshop on System-Level Interconnect Prediction archive
Proceedings of the 2002 international workshop on System-level interconnect prediction table of contents
San Diego, California, USA
SESSION: Using Prediction for Performance Optimization and Estimation table of contents
Pages: 85 - 89  
Year of Publication: 2002
ISBN:1-58113-481-9
Authors
Hongyu Chen  University of California, San Diego, La Jolla, CA
Changge Qiao  Synopsys, Inc., Mountain View, CA
Feng Zhou  University of California, San Diego, La Jolla, CA
Chung-Kuan Cheng  University of California, San Diego, La Jolla, CA
Sponsor
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 2,   Downloads (12 Months): 23,   Citation Count: 8
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ABSTRACT

We devised an efficient and accurate estimation of the rectilinear Steiner minimal tree (SMT), which is an essential building block for on-line and posteriori interconnect prediction. We proposed a new rectilinear Steiner tree generator, Refined Single Trunk Tree (RST-T). Compared with traditional minimal spanning tree based Steiner tree heuristics, RST-T has several advantages. 1. The algorithm runs very fast. Experiments show that RST-T algorithm runs 50 times faster than Prim's minimum spanning tree algorithm for 10-pin nets. 2. The RST-T provides excellent wire length estimation of the optimal solutions. For the nets of no more than 10 pins, the average wire length of RST-T is within 6 percent of the optimal solutions. Actually, for the nets with five or less pins, the wire length of RST-T is optimal. 3. The topology of RST-T remains stable when pin locations deviate. Experiments show that the topologies of routing trees produced by the proposed algorithm is much more stable than the minimum spanning tree and iterative 1-Steiner heuristics.


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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CITED BY  8

Collaborative Colleagues:
Hongyu Chen: colleagues
Changge Qiao: colleagues
Feng Zhou: colleagues
Chung-Kuan Cheng: colleagues