DocumentCode :
2298454
Title :
Learning as applied to stochastic optimization for standard cell placement
Author :
Su, Lixin ; Buntine, Wray ; Newton, A. Richard ; Peters, Bradley S.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
fYear :
1998
fDate :
5-7 Oct 1998
Firstpage :
622
Lastpage :
627
Abstract :
Although becoming increasingly important, stochastic algorithms are often slow since a large number of random design perturbations are required to achieve an acceptable result-they have no built-in “intelligence”. In this work, we used regression to learn the swap evaluation function while simulated annealing is applied to 2D standard-cell placement problem. The learned evaluation function is then applied to the trained simulated annealing algorithm (TSA). The annealing quality improvement of TSA was 15%~43% for the set of examples used in learning and 7%~21% for new examples. With the same amount of CPU time, TSA could improve the annealing quality by up to 28% for some benchmark circuits we tested. In addition the use of the evaluation function successfully predicted the effect of the windowed sampling technique and derived the informally accepted advantages of windowing from the test set automatically
Keywords :
circuit layout CAD; circuit optimisation; simulated annealing; evaluation function; random design perturbations; simulated annealing; standard cell placement; stochastic optimization; windowed sampling; Algorithm design and analysis; Circuit testing; Clustering algorithms; Cost function; Design optimization; Sampling methods; Simulated annealing; Stochastic processes; Tellurium; Timing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Design: VLSI in Computers and Processors, 1998. ICCD '98. Proceedings. International Conference on
Conference_Location :
Austin, TX
ISSN :
1063-6404
Print_ISBN :
0-8186-9099-2
Type :
conf
DOI :
10.1109/ICCD.1998.727129
Filename :
727129
Link To Document :
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