Evaluation of Fuzzy ARTMAP with DBSCAN in VLSI Application
The various applications of VLSI circuits in highperformance
computing, telecommunications, and consumer
electronics has been expanding progressively, and at a very hasty
pace. This paper describes a new model for partitioning a circuit
using DBSCAN and fuzzy ARTMAP neural network. The first step
is concerned with feature extraction, where we had make use
DBSCAN algorithm. The second step is the classification and is
composed of a fuzzy ARTMAP neural network. The performance of
both approaches is compared using benchmark data provided by
MCNC standard cell placement benchmark netlists. Analysis of the
investigational results proved that the fuzzy ARTMAP with
DBSCAN model achieves greater performance then only fuzzy
ARTMAP in recognizing sub-circuits with lowest amount of
interconnections between them The recognition rate using fuzzy
ARTMAP with DBSCAN is 97.7% compared to only fuzzy
ARTMAP.
[1] C.J. Alpert and A.B. Kahng, "RecentDirections in Net list Partitioning:A
Survey," Integration: the VLSIJournal, 19(1-2), 1995, pp. 1 - 81.
[2] Giovanni De Micheli and Rajesh K. Gupta. Hardware/software codesign.
Proceedings of the IEEE, 85(3):349-365, March 1997.
[3] Jan Madsen, Jesper Grode, and Peter V. Knudsen. Hardware/Software
Partitioning using the LYCOS System, chapter 9. Hardware/Software
Codesign: principles and Practice. Kluwer Academic Publishers,
Netherlands, 1997.
[4] M. Garey and D. Johnson. Computers and Intractability. Freeman, 1979.
[5] P. Chen and D. F. Wong, "On retiming for FPGA logic module
minimization, " Proc. IEEE Intl. Conference on Computer Design,
pp.394- 397, 1994.
[6] Y. P. Chen, T. C. Wang and D. F. Wong, "A graph partitioning problem
for multiple-chip design", Proc. Intl. Symposium on Circuits and
Systems, May 1993.
[7] Efficient partitioning of components, Annual ACM IEEE Design
Automation Conference Proceedings of the 5th annual workshop on
Design automation Washington, D. C., United States,1968, 16.1 - 16.21.
[8] B.W. Kernighan and S. Lin, "An Efficient Heuristic Procedure for
Partitioning Graphs", The Bell System Technical Journal, vol. 49, n. 2,
pp. 291-307, February1970.
[9] C.M. Fiduccia and R.M. Mattheyses, "A Linear-Time Heuristic for
Improving Network Partitions", inProceedings of > __DAC, pp.
175-181, Las Vegas,Nevada, June 1982, ACM/IEEE.
[10] S. Dutt and W. Deng, "VLSI Circuit Partitioning byCluster-Removal
Using Iterative Improvement Techniques", in IEEE International
Conference on CAD,pp. 194-200. ACM/IEEE, 1996.
[11] Carpenter, G.A., 1997, Distributed learning, recognition, and prediction
by ART and ARTMAP neural networks, Neural Networks, 10:1473-
1494.
[12] Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., and
Rosen, D.B., 1992, Fuzzy ARTMAP: neural network architecture for
Incremental supervised learning of analog multidimensional maps,
IEEE Transactions on Neural Networks, 3:698-713.
[13] Caudell, T.P., Smith, S.D.G., Escobedo, R., and Anderson, M., 1994,
NIRS: Large scale ART-1 neural architectures for engineering design
Retrieval, Neural Networks, 7:1339-1350.
[14] Benno Stein and Michael Busch, Second International Workshop on
Text- Based information Retrieval (TIR 05)Stein, Meyer zu Eißen
(Eds.)Fachberichte Informatik, pp. 45-56, ISSN 1860-4471c
University of Koblenz-Landau, Germany.
[15] Martin Ester,Hans-Peter Kriegel,Jorg Sander,Xiaowei Xu, Proceedings
of 2nd International Conference on Knowledge Discovery and Data
Mining (KDD-96).
[16] Busch. Analyse dichtebasierter Clusteralgorithmen am Beispiel von
DBSCAN und MajorClust. Study work, Paderborn University, Institute
for Computer Science, March 2005.
[17] Tan, S.C., Rao, M.V.C., and Lim, C.P. (2004b), "An adaptive fuzzy minmax
conflict-resolving classifier," in Proceedings of the 9th Online
World Conference on Soft Computing in Industrial Applications,
WSC9, 20 September - 8 October 2004.
[18] Dagher. I, Georgiopoulos. M, Heileman. G.L., Bebis, G. (1999), "An
ordering algorithm for pattern presentation in fuzzy ARTMAP that
tends to improve generalization performance," IEEE Trans Neural
Networks, vol. 10, pp. 768-778.
[1] C.J. Alpert and A.B. Kahng, "RecentDirections in Net list Partitioning:A
Survey," Integration: the VLSIJournal, 19(1-2), 1995, pp. 1 - 81.
[2] Giovanni De Micheli and Rajesh K. Gupta. Hardware/software codesign.
Proceedings of the IEEE, 85(3):349-365, March 1997.
[3] Jan Madsen, Jesper Grode, and Peter V. Knudsen. Hardware/Software
Partitioning using the LYCOS System, chapter 9. Hardware/Software
Codesign: principles and Practice. Kluwer Academic Publishers,
Netherlands, 1997.
[4] M. Garey and D. Johnson. Computers and Intractability. Freeman, 1979.
[5] P. Chen and D. F. Wong, "On retiming for FPGA logic module
minimization, " Proc. IEEE Intl. Conference on Computer Design,
pp.394- 397, 1994.
[6] Y. P. Chen, T. C. Wang and D. F. Wong, "A graph partitioning problem
for multiple-chip design", Proc. Intl. Symposium on Circuits and
Systems, May 1993.
[7] Efficient partitioning of components, Annual ACM IEEE Design
Automation Conference Proceedings of the 5th annual workshop on
Design automation Washington, D. C., United States,1968, 16.1 - 16.21.
[8] B.W. Kernighan and S. Lin, "An Efficient Heuristic Procedure for
Partitioning Graphs", The Bell System Technical Journal, vol. 49, n. 2,
pp. 291-307, February1970.
[9] C.M. Fiduccia and R.M. Mattheyses, "A Linear-Time Heuristic for
Improving Network Partitions", inProceedings of > __DAC, pp.
175-181, Las Vegas,Nevada, June 1982, ACM/IEEE.
[10] S. Dutt and W. Deng, "VLSI Circuit Partitioning byCluster-Removal
Using Iterative Improvement Techniques", in IEEE International
Conference on CAD,pp. 194-200. ACM/IEEE, 1996.
[11] Carpenter, G.A., 1997, Distributed learning, recognition, and prediction
by ART and ARTMAP neural networks, Neural Networks, 10:1473-
1494.
[12] Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., and
Rosen, D.B., 1992, Fuzzy ARTMAP: neural network architecture for
Incremental supervised learning of analog multidimensional maps,
IEEE Transactions on Neural Networks, 3:698-713.
[13] Caudell, T.P., Smith, S.D.G., Escobedo, R., and Anderson, M., 1994,
NIRS: Large scale ART-1 neural architectures for engineering design
Retrieval, Neural Networks, 7:1339-1350.
[14] Benno Stein and Michael Busch, Second International Workshop on
Text- Based information Retrieval (TIR 05)Stein, Meyer zu Eißen
(Eds.)Fachberichte Informatik, pp. 45-56, ISSN 1860-4471c
University of Koblenz-Landau, Germany.
[15] Martin Ester,Hans-Peter Kriegel,Jorg Sander,Xiaowei Xu, Proceedings
of 2nd International Conference on Knowledge Discovery and Data
Mining (KDD-96).
[16] Busch. Analyse dichtebasierter Clusteralgorithmen am Beispiel von
DBSCAN und MajorClust. Study work, Paderborn University, Institute
for Computer Science, March 2005.
[17] Tan, S.C., Rao, M.V.C., and Lim, C.P. (2004b), "An adaptive fuzzy minmax
conflict-resolving classifier," in Proceedings of the 9th Online
World Conference on Soft Computing in Industrial Applications,
WSC9, 20 September - 8 October 2004.
[18] Dagher. I, Georgiopoulos. M, Heileman. G.L., Bebis, G. (1999), "An
ordering algorithm for pattern presentation in fuzzy ARTMAP that
tends to improve generalization performance," IEEE Trans Neural
Networks, vol. 10, pp. 768-778.
@article{"International Journal of Information, Control and Computer Sciences:60884", author = "K. A. Sumithradevi and Vijayalakshmi. M. N. and Annamma Abraham. and Dr. Vasanta", title = "Evaluation of Fuzzy ARTMAP with DBSCAN in VLSI Application", abstract = "The various applications of VLSI circuits in highperformance
computing, telecommunications, and consumer
electronics has been expanding progressively, and at a very hasty
pace. This paper describes a new model for partitioning a circuit
using DBSCAN and fuzzy ARTMAP neural network. The first step
is concerned with feature extraction, where we had make use
DBSCAN algorithm. The second step is the classification and is
composed of a fuzzy ARTMAP neural network. The performance of
both approaches is compared using benchmark data provided by
MCNC standard cell placement benchmark netlists. Analysis of the
investigational results proved that the fuzzy ARTMAP with
DBSCAN model achieves greater performance then only fuzzy
ARTMAP in recognizing sub-circuits with lowest amount of
interconnections between them The recognition rate using fuzzy
ARTMAP with DBSCAN is 97.7% compared to only fuzzy
ARTMAP.", keywords = "VLSI, Circuit partitioning, DBSCAN, fuzzyARTMAP.", volume = "1", number = "12", pages = "4012-4", }
