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A parallel and randomized algorithm for large-scale discrete dual-Vt assignment and continuous gate sizing
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International Symposium on Low Power Electronics and Design archive
Proceeding of the 13th international symposium on Low power electronics and design table of contents
Bangalore, India
SESSION: Power optimization table of contents
Pages 45-50  
Year of Publication: 2008
ISBN:978-1-60558-109-5
Authors
Tai-Hsuan Wu  University of Wisconsin, Madison, WI, USA
Lin Xie  University of Wisconsin, Madison, WI, USA
Azadeh Davoodi  University of Wisconsin, Madison, WI, USA
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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ABSTRACT

We propose a parallel and randomized algorithm to solve the problem of discrete dual-Vt assignment combined with continuous gate sizing which is an important low power design technique in high performance domains. This combinatorial optimization problem is particularly difficult to solve on large-sized circuits. We first introduce a hybrid algorithm which combines the existing heuristics and convex formulations for this problem to achieve a better tradeoff between the runtime of the algorithm and the quality of generated solution. We then extend our algorithm to include parallelism and randomization. We introduce a unique utilization of parallelism to better identify the optimization direction. Consequently, we can reduce both the number of iterations in optimization as well as improve the quality of solution. We further use random sampling to avoid being trapped in local minima and to focus the optimization effort on the more "promising" regions of the solution space. Our algorithm improves the average power by 37% compared to an approach which is based on solving a continuous convex program and applying discretization. Power improvement is over 50% for larger benchmarks for an implementation on a grid of 9 computers.


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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H. Chou, Y.-H. Wang, and C. C.-P. Chen. Fast and effective gate-sizing with multiple-vt assignment using generalized lagrangian relaxation. 2005.
 
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iscas benchmarks available at:. http://www.fm.vslib.cz/~kes/asic/iscas/.
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mosek optimization. available at: http://www.mosek.com.
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T. Tannenbaum, D. Wright, K. Miller, and M. Livny. Condor -- A Distributed Job Scheduler. MIT Press, October 2001.
 
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H. Tennakoon and C. Sechen. Gate sizing using lagrangian relaxation combined with a fast gradient-based pre-processing step. Proc. Des. Autom. Conference, 2007.
 
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N. R. Tool. available at: http://www.nlreg.com.
 
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Collaborative Colleagues:
Tai-Hsuan Wu: colleagues
Lin Xie: colleagues
Azadeh Davoodi: colleagues