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Scalable exploration of functional dependency by interpolation and incremental SAT solving
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Source International Conference on Computer Aided Design archive
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design table of contents
San Jose, California
SESSION: Applications of SAT and QBF table of contents
Pages 227-233  
Year of Publication: 2007
ISBN ~ ISSN:1092-3152 , 1-4244-1382-6
Authors
Chih-Chun Lee  National Taiwan University, Taipei, Taiwan
Jie-Hong R. Jiang  National Taiwan University, Taipei, Taiwan
Chung-Yang (Ric) Huang  National Taiwan University, Taipei, Taiwan
Alan Mishchenko  University of California, Berkeley, CA
Sponsors
: IEEE CASS/CANDE
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CS\DATC : IEEE Computer Society
CEDA : Council on Electronic Design Automation
Publisher
IEEE Press  Piscataway, NJ, USA
Bibliometrics
Downloads (6 Weeks): 8,   Downloads (12 Months): 59,   Citation Count: 7
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ABSTRACT

Functional dependency is concerned with rewriting a Boolean function f as a function h over a set of base functions {g1, ..., gn}, i.e. f = h(g1, ..., gn). It plays an important role in many aspects of electronic design automation (EDA), ranging from logic synthesis to formal verification. Prior approaches to the exploration of functional dependency are based on binary decision diagrams (BDDs), which may not be easily scalable to large designs. This paper proposes a novel reformulation that extensively exploits the capability of modern satisfiability (SAT) solvers. Thereby, functional dependency is detected effectively through incremental SAT solving, and the dependency function h, if it exists, is obtained through Craig interpolation. The main strengths of the proposed approach include: (1) fast detection of functional dependency with modest memory consumption and thus scalable to large designs, (2) a full capacity to handle a large set of base functions and thus discovering dependency whenever exists, and (3) potential application to large-scale logic optimization and verification reduction. Experimental results show the proposed method is far superior to prior work and scales well in dealing with the largest ISCAS89 and ITC99 benchmark circuits with up to 200K gates.


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.

 
1
J.-H. R. Jiang and R. K. Brayton. Functional dependency for verification reduction. In Proc. CAV, pp. 268--280, 2004.
 
2
 
3
B. Lin and A. R. Newton. Exact redundant state registers removal based on binary decision diagrams. In Proc. Int'l Conf. Very Large Scale Integration, pp. 277--286, 1991.
4
5
 
6
C. Berthet, O. Coudert, and J.-C. Madre. New ideas on symbolic manipulations of finite state machines. In Proc. ICCD, pp. 224--227, 1990.
 
7
 
8
E. Gregoire, R. Ostrowski, B. Mazure, and L. Sais. Automatic extraction of functional dependencies. In Proc. SAT, 2004.
 
9
10
 
11
N. Eén and N. Sörensson. An extensible SAT-solver. In Proc. SAT, pp. 502--518, 2003.
 
12
 
13
A. Mishchenko, J. Zhang, S. Sinha, J. Burch, R. K. Brayton, and M. Chrzanowska-Jeske. Using simulation and satisfiability to compute flexibilities in Boolean networks. IEEE Trans. on CAD, vol. 25, no. 5, pp. 742--755, 2006.
 
14
W. Craig. Linear reasoning: A new form of the Herbrand-Gentzen theorem. J. Symbolic Logic, 22(3):250--268, 1957.
15
 
16
J. Krajicek. Interpolation theorems, lower bounds for proof systems, and independence results for bounded arithmetic. J. Symbolic Logic, 62(2):457--486, June 1997.
 
17
P. Pudlak. Lower bounds for resolution and cutting plane proofs and monotone computations. J. Symbolic Logic, 62(3):981--998, September 1997.
 
18
K. L. McMillan. Interpolation and SAT-based model checking. In Proc. CAV, pp. 1--13, 2003.
 
19
G. Tseitin. On the complexity of derivation in propositional calculus. Studies in Constructive Mathematics and Mathematical Logic, pp. 466--483, 1970.
 
20
21
 
22
R. Jhala and K. L. McMillan, "Interpolant-based transition relation approximation". Proc. CAV, pp. 39--51, 2005.
 
23
Berkeley Logic Synthesis and Verification Group. ABC: A System for Sequential Synthesis and Verification. Release 51205. http://www.eecs.berkeley.edu/~alanmi/abc/
 
24
 
25
Y. Novikov and R. Brinkmann. Foundations of Hierarchical SAT-Solving. In Proc. Int'l Workshop on Boolean Problems, 2004.
 
26
A. Mishchenko, R. Brayton, J.-H. R. Jiang, and S. Jang. SAT-based logic optimization and resynthesis. In Proc. IWLS, pp. 358--364, 2007.

CITED BY  7
 
 
 
Collaborative Colleagues:
Chih-Chun Lee: colleagues
Jie-Hong R. Jiang: colleagues
Chung-Yang (Ric) Huang: colleagues
Alan Mishchenko: colleagues