Abstract
As operation frequencies and integration densities of modern very large-scale integration (VLSI) circuits increase while device sizes shrink, the quest for high-speed VLSI applications has highlighted the negligible effects of interconnects. It is important to minimize the interconnect wire lengths during VLSI physical design stage. This paper focuses on the minimization process of the total wire length after placement, that is, macro-cell orientation. A novel evolutionary neural network approach based on the concept of evolutionary programming (EPENN) is proposed to address this combinatorial optimization problem. Numerical experiments and simulation results have shown that the presented approach can obtain high quality solutions with low computational complexity.
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References
Achar, R., Nakhla, M.S.: Simulation of High-Speed Interconnects. Proceedings of the IEEE 89(5), 693–728 (2001)
Nakhla, M., Ushida, A.: Special Issue on Simulation, Modeling, and Electrical Design of High-Speed Interconnects. IEEE Trans, Circuits Syst. 47, 44–45 (2000)
Gao, D.S., Yang, A.T., Kang, S.M.: Modeling and Simulation of Interconnection Delays and Crosstalk in High-Speed IC. IEEE Trans, Circuit Syst. 37, 1–9 (1990)
Cong, J.: An Interconnect-Centric Design Flow for Nanometer Technologies. In: Proc. of Int’l Symp. on VLSI Technology, System, and Applications, pp. 54–57 (1999)
Breuer, M.A., Sarrafzadeh, M., Somenzi, F.: Fundamental CAD Algorithms. IEEE Trans, CAD of Integrated Circuits and Syst. 19(12), 1449–1475 (2000)
Nan, G.F., Li, M.Q.: Application of Evolutionary Algorithm to Three Key Problems in VLSI Layout. In: Proceedings of the 4th ICMLC, Guangzhou (2005)
Mazumder, P.: Genetic Algorithms for VLSI Design, Layout & Test Automation. Prentice-Hall, Englewood Cliffs (1999)
Yao, B., Hou, W.T., Hong, X.L., Cai, Y.C.: FAME: A Fast Detailed Placement Algorithm for Standard Cell Layout Based on Mixed Min-cut and Enumeration. Chinese Journal of Semiconductors 21, 744–753 (2005)
Gao, W.: Study on New Evolutionary Neural Network. In: Proc. of 3rd ICMLC, Xi’an (2003)
Fang, J., Xi, Y.G.: Neural Network Design Based on Evolutionary Programming. Artificial Intelligence in Engineering, pp. 155–161. Elsevier, Amsterdam (1997)
Yamada, M., Liu, C.L.: An Analytical Method for Optimal Module Orientation. In: Proc. Int. Symp. Circuits and Systems, pp. 1679–1682 (1988)
Libeskind-Hadas, R., Liu, C.L.: Solutions to the Module Orientation and Rotation Problems by Neural Computation Networks. In: Proc. 26th ACM/IEEE Design Automation Conf., pp. 400–405 (1989)
Lee, K.C., Takefuji, Y.: A Generalized Maximum Neural Network for the Module Orientation Problem. Int. J. Electron. 72(3), 331–355 (1992)
Funabiki, N., Kitamichi, J., Nishikawa, S.: An Evolutionary Neural Network Approach for Module Orientation Problems. IEEE Trans, Systems, Man, and Cybernetics Part B: Cybernetics 28(6), 849–856 (1998)
Gloria, G., Jose, M.: Design and Analysis of Maximum Hopfield Networks. IEEE Trans, Neural Networks 12(2), 329–339 (2001)
Angeline, P.J., Saunders, G.M., Pollack, J.: An Evolutionary Algorithm that Constructs Neural Networks. IEEE Trans, Neural Networks 5, 54–65 (1994)
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Zhou, W., Wang, G., Chen, X. (2006). Application of a Novel Evolutionary Neural Network for Macro-cell Placement Optimization in VLSI Physical Design. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_77
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DOI: https://doi.org/10.1007/11816157_77
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