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Digital Circuit Optimization via Geometric Programming

Author

Listed:
  • Stephen P. Boyd

    (Department of Electrical Engineering, Stanford University, Stanford, California 94305-9510)

  • Seung-Jean Kim

    (Department of Electrical Engineering, Stanford University, Stanford, California 94305-9510)

  • Dinesh D. Patil

    (Department of Electrical Engineering, Stanford University, Stanford, California 94305-9510)

  • Mark A. Horowitz

    (Department of Electrical Engineering, Stanford University, Stanford, California 94305-9510)

Abstract

This paper concerns a method for digital circuit optimization based on formulating the problem as a geometric program (GP) or generalized geometric program (GGP), which can be transformed to a convex optimization problem and then very efficiently solved. We start with a basic gate scaling problem, with delay modeled as a simple resistor-capacitor (RC) time constant, and then add various layers of complexity and modeling accuracy, such as accounting for differing signal fall and rise times, and the effects of signal transition times. We then consider more complex formulations such as robust design over corners, multimode design, statistical design, and problems in which threshold and power supply voltage are also variables to be chosen. Finally, we look at the detailed design of gates and interconnect wires, again using a formulation that is compatible with GP or GGP.

Suggested Citation

  • Stephen P. Boyd & Seung-Jean Kim & Dinesh D. Patil & Mark A. Horowitz, 2005. "Digital Circuit Optimization via Geometric Programming," Operations Research, INFORMS, vol. 53(6), pages 899-932, December.
  • Handle: RePEc:inm:oropre:v:53:y:2005:i:6:p:899-932
    DOI: 10.1287/opre.1050.0254
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    References listed on IDEAS

    as
    1. Bajis Dodin, 1984. "Determining the K Most Critical Paths in PERT Networks," Operations Research, INFORMS, vol. 32(4), pages 859-877, August.
    2. Arfst Ludwig & Rolf Möhring & Frederik Stork, 2001. "A Computational Study on Bounding the Makespan Distribution in Stochastic Project Networks," Annals of Operations Research, Springer, vol. 102(1), pages 49-64, February.
    3. George B. Kleindorfer, 1971. "Bounding Distributions for a Stochastic Acyclic Network," Operations Research, INFORMS, vol. 19(7), pages 1586-1601, December.
    4. Richard M. Van Slyke, 1963. "Letter to the Editor---Monte Carlo Methods and the PERT Problem," Operations Research, INFORMS, vol. 11(5), pages 839-860, October.
    5. H. O. Hartley & A. W. Wortham, 1966. "A Statistical Theory for PERT Critical Path Analysis," Management Science, INFORMS, vol. 12(10), pages 469-481, June.
    6. R. A. Bowman, 1995. "Efficient Estimation of Arc Criticalities in Stochastic Activity Networks," Management Science, INFORMS, vol. 41(1), pages 58-67, January.
    7. Luc P. Devroye, 1979. "Inequalities for the Completion Times of Stochastic PERT Networks," Mathematics of Operations Research, INFORMS, vol. 4(4), pages 441-447, November.
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    Citations

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    Cited by:

    1. Angelo Ciccazzo & Gianni Di Pillo & Vittorio Latorre, 2015. "A SVM Surrogate Model Based Method for Yield Optimization in Electronic Circuit Design," DIAG Technical Reports 2015-03, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    2. Warren P. Adams & Stephen M. Henry, 2012. "Base-2 Expansions for Linearizing Products of Functions of Discrete Variables," Operations Research, INFORMS, vol. 60(6), pages 1477-1490, December.
    3. Hao-Chun Lu, 2017. "Improved logarithmic linearizing method for optimization problems with free-sign pure discrete signomial terms," Journal of Global Optimization, Springer, vol. 68(1), pages 95-123, May.
    4. Wolfram Wiesemann & Daniel Kuhn & Berc Rustem, 2009. "Robust Resource Allocations in Temporal Networks," Working Papers 020, COMISEF.
    5. Lu, Hao-Chun, 2020. "Indicator of power convex and exponential transformations for solving nonlinear problems containing posynomial terms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    6. Guillermo Angeris & Akshay Agrawal & Alex Evans & Tarun Chitra & Stephen Boyd, 2021. "Constant Function Market Makers: Multi-Asset Trades via Convex Optimization," Papers 2107.12484, arXiv.org.
    7. Huan Xu & Constantine Caramanis & Shie Mannor, 2012. "A Distributional Interpretation of Robust Optimization," Mathematics of Operations Research, INFORMS, vol. 37(1), pages 95-110, February.
    8. Seyed Ahmad Yazdian & Kamran Shahanaghi & Ahmad Makui, 2016. "Joint optimisation of price, warranty and recovery planning in remanufacturing of used products under linear and non-linear demand, return and cost functions," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(5), pages 1155-1175, April.
    9. Belleh Fontem, 2023. "Robust Chance-Constrained Geometric Programming with Application to Demand Risk Mitigation," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 765-797, May.
    10. Xinlei Wang & Johan Lim & Seung-Jean Kim & Kyu Hahn, 2015. "Estimating cell probabilities in contingency tables with constraints on marginals/conditionals by geometric programming with applications," Computational Statistics, Springer, vol. 30(1), pages 107-129, March.
    11. Amir Ardestani-Jaafari & Erick Delage, 2016. "Robust Optimization of Sums of Piecewise Linear Functions with Application to Inventory Problems," Operations Research, INFORMS, vol. 64(2), pages 474-494, April.
    12. Hao-Chun Lu & Liming Yao, 2019. "Efficient Convexification Strategy for Generalized Geometric Programming Problems," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 226-234, April.
    13. Hua Zhou & Kenneth Lange, 2015. "Path following in the exact penalty method of convex programming," Computational Optimization and Applications, Springer, vol. 61(3), pages 609-634, July.
    14. Han-Lin Li & Hao-Chun Lu, 2009. "Global Optimization for Generalized Geometric Programs with Mixed Free-Sign Variables," Operations Research, INFORMS, vol. 57(3), pages 701-713, June.
    15. Angelo Ciccazzo & Vittorio Latorre & Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2015. "Derivative-Free Robust Optimization for Circuit Design," Journal of Optimization Theory and Applications, Springer, vol. 164(3), pages 842-861, March.
    16. Rashed Khanjani-Shiraz & Salman Khodayifar & Panos M. Pardalos, 2021. "Copula theory approach to stochastic geometric programming," Journal of Global Optimization, Springer, vol. 81(2), pages 435-468, October.

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