McPOWER: a Monte Carlo approach to power estimation

Excessive power dissipation in integrated circuits causes overheating and can lead to soft errors and/or permanent damage. The severity of the problem increases in proportion to the level of integration, so that power estimation tools are badly needed for present-day technology. Traditional simulation-based approaches simulate the circuit using test/functional input pattern sets. This is expensive and does not guarantee a meaningful power value. Other recent approaches have used probabilistic techniques in order to cover a large set of inputs patterns. However, they trade-o accuracy for speed in ways that are not always acceptable. In this paper, we investigate an alternative technique that combines the accuracy of simulation-based techniques with the speed of the probabilistic techniques. The resulting method is statistical in nature; it consists of applying randomly-generated input patterns to the circuit and monitoring, with a simulator, the resulting power value. This is continued until a value of power is obtained with a desired accuracy, at a speci ed con dence level. We present the algorithm and experimental results, and discuss the superiority of this new approach.

[1]  Hendrikus J. M. Veendrick,et al.  Short-circuit dissipation of static CMOS circuitry and its impact on the design of buffer circuits , 1984 .

[2]  Farid N. Najm,et al.  Transition density, a stochastic measure of activity in digital circuits , 1991, 28th ACM/IEEE Design Automation Conference.

[3]  Yan-Chyuan Shiau,et al.  Time domain current waveform simulation of CMOS circuits , 1988, [1988] IEEE International Conference on Computer-Aided Design (ICCAD-89) Digest of Technical Papers.

[4]  Farid N. Najm,et al.  Transition density: a new measure of activity in digital circuits , 1993, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[5]  C.M. Huizer,et al.  Power Dissipation Analysis of CMOS VLSI Circuits by means of Switch-Level Simulation , 1990, ESSCIRC '90: Sixteenth European Solid-State Circuits Conference.

[6]  H. Saunders,et al.  Probability, Random Variables and Stochastic Processes (2nd Edition) , 1989 .

[7]  A. Bowker,et al.  Statistical Theory with Engineering Applications. , 1953 .

[8]  F. Brglez,et al.  A neutral netlist of 10 combinational benchmark circuits and a target translator in FORTRAN , 1985 .

[9]  Richard L. Scheaffer,et al.  Probability and statistics for engineers , 1986 .

[10]  G. Y. Yacoub,et al.  An accurate simulation technique for short-circuit power dissipation based on current component isolation , 1989, IEEE International Symposium on Circuits and Systems,.

[11]  Sung-Mo Kang.  Accurate simulation of power dissipation in VLSI circuits , 1986 .

[12]  G. Lieberman,et al.  Statistical Theory with Engineering Applications , 1952 .

[13]  John E. Freund,et al.  Probability and statistics for engineers , 1965 .

[14]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.