Why Quasi-Monte Carlo is Better Than Monte Carlo or Latin Hypercube Sampling for Statistical Circuit Analysis
暂无分享,去创建一个
[1] DAVID G. KENDALL,et al. Introduction to Mathematical Statistics , 1947, Nature.
[2] M. E. Muller,et al. A Note on the Generation of Random Normal Deviates , 1958 .
[3] J. Halton. On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .
[4] J. Hammersley. MONTE CARLO METHODS FOR SOLVING MULTIVARIABLE PROBLEMS , 1960 .
[5] E. Hlawka. Funktionen von beschränkter Variatiou in der Theorie der Gleichverteilung , 1961 .
[6] J. Kiefer. On large deviations of the empiric D. F. of vector chance variables and a law of the iterated logarithm. , 1961 .
[7] Edwin Hewitt,et al. Real And Abstract Analysis , 1967 .
[8] I. Sobol. On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .
[9] James L. Massey,et al. Review of 'Error-Correcting Codes, 2nd edn.' (Peterson, W. W., and Weldon, E. J., Jr.; 1972) , 1973, IEEE Trans. Inf. Theory.
[10] Franklin A. Graybill,et al. Theory and Application of the Linear Model , 1976 .
[11] H. Niederreiter. Quasi-Monte Carlo methods and pseudo-random numbers , 1978 .
[12] I. A. Antonov,et al. An economic method of computing LPτ-sequences , 1979 .
[13] G. Reinsel,et al. Introduction to Mathematical Statistics (4th ed.). , 1980 .
[14] H. Keng,et al. Applications of number theory to numerical analysis , 1981 .
[15] K. Singhal,et al. Statistical design centering and tolerancing using parametric sampling , 1981 .
[16] H. Faure. Discrépance de suites associées à un système de numération (en dimension s) , 1982 .
[17] Ieee Circuits,et al. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems information for authors , 2018, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[18] Timothy N. Trick,et al. A Study of Variance Reduction Techniques for Estimating Circuit Yields , 1983, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[19] M. Stein. Large sample properties of simulations using latin hypercube sampling , 1987 .
[20] Sung-Mo Kang,et al. Statistical Performance Modeling and Parametric Yield Estimation of MOS VLSI , 1987, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[21] Paul Bratley,et al. Algorithm 659: Implementing Sobol's quasirandom sequence generator , 1988, TOMS.
[22] H. Niederreiter. Low-discrepancy and low-dispersion sequences , 1988 .
[23] F. A. Seiler,et al. Numerical Recipes in C: The Art of Scientific Computing , 1989 .
[24] Hany L. Abdel-Malek,et al. The ellipsoidal technique for design centering and region approximation , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..
[25] Harald Niederreiter,et al. Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.
[26] N. J. Elias. Acceptance sampling: An efficient, accurate method for estimating and optimizing parametric yield , 1993, Proceedings of IEEE Custom Integrated Circuits Conference - CICC '93.
[27] Sung-Mo Kang,et al. Convexity-based algorithms for design centering , 1993, ICCAD '93.
[28] Peter Feldmann,et al. Integrated circuit quality optimization using surface integrals , 1993, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..
[29] Robert G. Meyer,et al. Analysis and Design of Analog Integrated Circuits , 1993 .
[30] Makoto Matsumoto,et al. Twisted GFSR generators II , 1994, TOMC.
[31] N. J. Elias. Acceptance sampling: an efficient, accurate method for estimating and optimizing parametric yield /spl lsqb/IC manufacture/spl rsqb/ , 1994 .
[32] Kurt Antreich,et al. Circuit analysis and optimization driven by worst-case distances , 1994, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..
[33] H. Niederreiter,et al. A construction of low-discrepancy sequences using global function fields , 1995 .
[34] S. Tezuka. Uniform Random Numbers: Theory and Practice , 1995 .
[35] Shu Tezuka,et al. Uniform Random Numbers , 1995 .
[36] A. Owen. Randomly Permuted (t,m,s)-Nets and (t, s)-Sequences , 1995 .
[37] S. Tezuka,et al. Toward real-time pricing of complex financial derivatives , 1996 .
[38] R. Caflisch,et al. Smoothness and dimension reduction in Quasi-Monte Carlo methods , 1996 .
[39] A. Owen. Scrambled net variance for integrals of smooth functions , 1997 .
[40] A. Owen,et al. Valuation of mortgage-backed securities using Brownian bridges to reduce effective dimension , 1997 .
[41] Fred J. Hickernell,et al. A generalized discrepancy and quadrature error bound , 1998, Math. Comput..
[42] P. Glasserman,et al. A Comparison of Some Monte Carlo and Quasi Monte Carlo Techniques for Option Pricing , 1998 .
[43] K. Sakui,et al. A CMOS bandgap reference circuit with sub-1-V operation , 1999 .
[44] M. Keramat,et al. A novel approach to efficient yield estimation for microwave integrated circuits , 1999, 42nd Midwest Symposium on Circuits and Systems (Cat. No.99CH36356).
[45] Fred J. Hickernell,et al. Extensible Lattice Sequences for Quasi-Monte Carlo Quadrature , 2000, SIAM J. Sci. Comput..
[46] Richard J. Beckman,et al. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.
[47] Kai-Tai Fang,et al. The effective dimension and quasi-Monte Carlo integration , 2003, J. Complex..
[48] Paul Glasserman,et al. Monte Carlo Methods in Financial Engineering , 2003 .
[49] Art B. Owen,et al. Variance with alternative scramblings of digital nets , 2003, TOMC.
[50] Fred J. Hickernell,et al. Algorithm 823: Implementing scrambled digital sequences , 2003, TOMS.
[51] Frances Y. Kuo,et al. Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator , 2003, TOMS.
[52] G. Ökten,et al. Randomized quasi-Monte Carlo methods in pricing securities , 2004 .
[53] Andrzej J. Strojwas,et al. Projection-based performance modeling for inter/intra-die variations , 2005, ICCAD-2005. IEEE/ACM International Conference on Computer-Aided Design, 2005..
[54] F. Lemmermeyer. Error-correcting Codes , 2005 .
[55] Joan Borrell,et al. A fast algorithm to compute irreducible and primitive polynomials in finite fields , 2005, Mathematical systems theory.
[56] Michael Orshansky,et al. An efficient algorithm for statistical minimization of total power under timing yield constraints , 2005, Proceedings. 42nd Design Automation Conference, 2005..
[57] George S. Fishman,et al. A First Course in Monte Carlo , 2005 .
[58] Yu Cao,et al. New Generation of Predictive Technology Model for Sub-45 nm Early Design Exploration , 2006, IEEE Transactions on Electron Devices.
[59] K. Ravindran,et al. First-Order Incremental Block-Based Statistical Timing Analysis , 2004, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[60] Rob A. Rutenbar,et al. From Finance to Flip Flops: A Study of Fast Quasi-Monte Carlo Methods from Computational Finance Applied to Statistical Circuit Analysis , 2007, 8th International Symposium on Quality Electronic Design (ISQED'07).
[61] Rob A. Rutenbar,et al. Beyond Low-Order Statistical Response Surfaces: Latent Variable Regression for Efficient, Highly Nonlinear Fitting , 2007, 2007 44th ACM/IEEE Design Automation Conference.
[62] J. Jaffari,et al. On efficient Monte Carlo-based Statistical Static Timing Analysis of digital circuits , 2008, 2008 IEEE/ACM International Conference on Computer-Aided Design.
[63] Rob A. Rutenbar,et al. Practical, fast Monte Carlo statistical static timing analysis: Why and how , 2008, 2008 IEEE/ACM International Conference on Computer-Aided Design.
[64] David Blaauw,et al. Efficient Monte Carlo based incremental statistical timing analysis , 2008, 2008 45th ACM/IEEE Design Automation Conference.
[65] I. Sloan,et al. Low discrepancy sequences in high dimensions: How well are their projections distributed? , 2008 .
[66] Rob A. Rutenbar,et al. Novel Algorithms for Fast Statistical Analysis of Scaled Circuits , 2009, Lecture Notes in Electrical Engineering.