Abstract
Placement of standard cells is a part of physical VLSI chip design. In order to achieve high performance, area of the chip and lengths of wires connecting cells have to be minimized. In the placement step, the goal is to place cells in such a way that total wire-length is as short as possible. Since this problem is NP-hard, heuristic techniques have to be applied. Modern approaches include simulated annealing and genetic algorithms. In this paper, we discuss those methods and show that they can be improved by combination. A heuristic technique called parallel recombinative simulated annealing (PRSA) is described. It integrates features of both simulated annealing and genetic algorithms. Behavior of PRSA is studied with respect to different parameter settings.
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References
Aarts, E.H.L., Korst, J.H.M.: Simulated Annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing; New York 1989.
Brown, D.E., Huntley, C.L., Spillane, A.R.: A parallel genetic heuristic for the quadratic assignment problem; in: Schaffer, J.D. (eds.): Proceedings of the Third International Conference on Genetic Algorithms; San Mateo, CA 1989, pp. 406–415.
Chang, S.: The generation of minimal spanning trees with a Steiner topology; in: Journal of the ACM 19 (1972) 4, pp. 699–711.
Chen, H., Flenn, N.S.: Parallel Simulated Annealing and Genetic Algorithms: A Space of Hybrid Models; in: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.): Parallel Problem Solving from Nature — PPSN III; Berlin 1994, pp. 428–439.
Davis, L.: Handbook of Genetic Algorithms; New York 1991.
Diekmann, R., Lüling, R., Simon, J.: Problem independent simulated annealing and its application; Technical Report, University of Paderborn, Germany 1992.
Goldberg, D.E.: A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing; in: Complex Systems 4 (1990) 4, pp. 445–460.
Hoffmann, K.H., Sibani, P., Pedersen, J.M., Salamon, P.: Optimal Ensemble Size for Parallel Implementations of Simulated Annealing; in: Applied Mathematics Letters (1990) 3, pp. 53–61.
Kirkpatrick, S., Gellat, S.D., Vecchi, M.P.: Optimization by Simulated Annealing; in: Science 220 (1983) 5, pp. 671–680.
Kling, R.M., Banerjee, P.: ESP — A new standard cell placement package using simulated evolution; in: Proceedings of 24th ACM/IEEE Design Automation Conference; New York 1987, pp. 60–66.
Laursen, P.S.: Problem-Independent Parallel Simulated Annealing Using Selection and Migration; in: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.): Parallel Problem Solving from Nature — PPSN III; Berlin 1994, pp. 408–417.
Lin, F.-T., Kao, C.-Y., Hsu, C.-C.: Incorporating genetic algorithms into simulated annealing; in: Cantu-Ortiz, F. J., Terashima-Marin, H. (eds.): Proceedings of International Symposium on Artificial Intelligence; Cancun, Mexico 1991, pp. 290–297.
Mahfoud, W.S., Goldberg, D.E.: Parallel recombinative simulating annealing: a genetic algorithm; Technical Report No. 92002, University of Illinois, 1992.
Mallela, S., Grover, L.K.: Clustering Based Simulated Annealing for Standard Cell Placement; in: Proceedings of 25th ACM/IEEE Design Automation Conference; New York 1988, pp. 312–317.
Sechen, C., Lee, K.W.: An improved simulating annealing for row based placement; in: Proceedings of IEEE International Conference on Computer Aided Design; New York 1987, pp. 478–481.
Sechen, C., Sangiovanni-Vencentelli, A.: TimberWolf 3.2: A new standard cell placement and global routing package; in: Proceedings of 23rd Design Automation Conference; New York 1986, pp. 432–439.
Shahookar, K., Mazumder, P.: A genetic approach to standard cell placement using meta-genetic parameter optimization; in: IEEE Transactions on Computer-aided Design 9 (1990) 5, pp. 500–511.
Shahookar, K., Mazumder, P.: VLSI placement techniques; in: ACM Computing Surveys 23 (1991) 2, pp. 143–220.
Sherwani, N.: Algorithms for VLSI physical design automation; Boston 1993.
Singh, K., George, S.M., Rambabu, P.: Recurrent Networks for Standard Cell Placement; in: Balagurusamy, R., Sushila, B. (eds.): Artificial Intelligence Technology — Applications and Management; New Delhi, New York 1993, pp. 141–148.
Singh, K., Schneider, B., Kurbel, K.: Solving Sequencing Problems using Recurrent Neural Networks and Simulated Annealing — A Structural and Computational Comparison; to appear in: Proceedings of the International Conference on Operations Research (OR’94), Berlin 1994.
Azencott, R.: Simulated Annealing: Parallelization Techniques; New York 1992.
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Kurbel, K., Schneider, B., Singh, K. (1995). VLSI Standard Cell Placement by Parallel Hybrid Simulated-Annealing and Genetic Algorithm. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_127
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_127
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
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