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Exploring time/resource trade-offs by solving dual scheduling problems with the ant colony optimization
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ACM Transactions on Design Automation of Electronic Systems (TODAES) archive
Volume 12 ,  Issue 4  (September 2007) table of contents
Article No. 46  
Year of Publication: 2007
ISSN:1084-4309
Authors
Gang Wang  University of California, Santa Barbara, Santa Barbara, CA
Wenrui Gong  Mentor Graphics, Wilsonville, OR
Brian Derenzi  University of Washington, Seattle, WA
Ryan Kastner  University of California, Santa Barbara, Santa Barbara, CA
Publisher
ACM  New York, NY, USA
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ABSTRACT

Design space exploration during high-level synthesis is often conducted through ad hoc probing of the solution space using some scheduling algorithm. This is not only time consuming but also very dependent on designer's experience. We propose a novel design exploration method that exploits the duality of time- and resource-constrained scheduling problems. Our exploration automatically constructs a time/area tradeoff curve in a fast, effective manner. It is a general approach and can be combined with any high-quality scheduling algorithm. In our work, we use the max-min ant colony optimization technique to solve both time- and resource-constrained scheduling problems. Our algorithm provides significant solution-quality savings (average 17.3% reduction of resource counts) with similar runtime compared to using force-directed scheduling exhaustively at every time step. It also scales well across a comprehensive benchmark suite constructed with classic and real-life samples.


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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Aigner, G., Diwan, A., Heine, D. L., Moore, M. S. L. D. L., Murphy, B. R., and Sapuntzakis, C. 2000. The Basic SUIF Programming Guide. Computer Systems Laboratory, Stanford University Stanford, CA.
 
2
 
3
 
4
 
5
Dorigo, M., Maniezzo, V., and Colorni, A. 1996. Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part-B 26, 1 (Feb.), 29--41.
 
6
 
7
ExpressDFG. 2006. ExpressDFG benchmark website. http://express.ece.ucsb.edu/benchmark/.
 
8
Gutjahr, W. J. 2002. ACO algorithms with guaranteed convergence to the optimal solution. Inf. Process. Lett. 82, 3, 145--153.
9
 
10
11
 
12
Madsen, J., Grode, J., Knudsen, P. V., Petersen, M. E., and Haxthausen, A. 1997. LYCOS: The Lyngby co-synthesis system. Des. Autom. Embedded Syst. 2, 2 (Mar.), 125--63.
 
13
McFarland, M., Parker, A. C., and Camposano, R. 1990. The high-level synthesis of digital systems. In Proc. IEEE. 78, 301--318.
14
15
 
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Smith, M. D. and Holloway, G. 2002. An Introduction to Machine SUIF and Its Portable Libraries for Analysis and Optimization. Division of Engineering and Applied Sciences, Harvard University.
 
17
 
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Wang, G., Gong, W., DeRenzi, B., and Kastner, R. Ant colony optimizations for resource and timing constrained operation scheduling. IEEE Trans. Comput.-Aided Des (to appear).
 
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Wang, G., Gong, W., and Kastner, R. 2003. A new approach for task level computational resourcebi-partitioning. In Proceedings of the 15th International Conference on Parallel and Distributed Computingand Systems 1, 1 (Nov.), 439--444.
 
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Wang, G., Gong, W., and Kastner, R. 2004. System level partitioning for programmable platforms using the antcolony optimization. In Proceedings of the 13th International Workshop on Logic and Synthesis (IWLS).
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Collaborative Colleagues:
Gang Wang: colleagues
Wenrui Gong: colleagues
Brian Derenzi: colleagues
Ryan Kastner: colleagues