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Constrained Stimulus Generation with Self-Adjusting Using Tabu Search with Memory

Yanni ZHAO, Jinian BIAN, Shujun DENG, Zhiqiu KONG, Kang ZHAO

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Summary :

Despite the growing research effort in formal verification, industrial verification often relies on the constrained random simulation methodology, which is supported by constraint solvers as the stimulus generator integrated within simulator, especially for the large design with complex constraints nowadays. These stimulus generators need to be fast and well-distributed to maintain simulation performance. In this paper, we propose a dynamic method to guide stimulus generation by SAT solvers. An adjusting strategy named Tabu Search with Memory (TSwM) is integrated in the stimulus generator for the search and prune processes along with the constraint solver. Experimental results show that the method proposed in this paper could generate well-distributed stimuli with good performance.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.12 pp.3086-3093
Publication Date
2009/12/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.E92.A.3086
Type of Manuscript
Special Section PAPER (Special Section on VLSI Design and CAD Algorithms)
Category
Logic Synthesis, Test and Verfication

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