A simple unpredictable pseudo random number generator
SIAM Journal on Computing
Generation and application of pseudorandom sequences for random testing
Generation and application of pseudorandom sequences for random testing
Perfect local randomness in pseudo-random sequences
CRYPTO '89 Proceedings on Advances in cryptology
Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
Stratified random sampling for power estimation
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Power macromodeling for high level power estimation
DAC '97 Proceedings of the 34th annual Design Automation Conference
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
A power macromodeling technique based on power sensitivity
DAC '98 Proceedings of the 35th annual Design Automation Conference
Power invariant vector compaction based on bit clustering and temporal partitioning
ISLPED '98 Proceedings of the 1998 international symposium on Low power electronics and design
Design of practical and provably good random number generators
Journal of Algorithms - Special issue on SODA '95 papers
Analytical macromodeling for high-level power estimation
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
Stochastic sequential machine synthesis with application to constrained sequence generation
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Power estimation of behavioral descriptions
Proceedings of the conference on Design, automation and test in Europe
Stream synthesis for efficient power simulation based on spectral transforms
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Efficient, Perfect Random Number Generators
CRYPTO '88 Proceedings of the 8th Annual International Cryptology Conference on Advances in Cryptology
Clustered Table-Based Macromodels for RTL Power Estimation
GLS '99 Proceedings of the Ninth Great Lakes Symposium on VLSI
Analytical Model for High Level Power Modeling of Combinational and Sequential Circuits
VOLTA '99 Proceedings of the IEEE Alessandro Volta Memorial Workshop on Low-Power Design
Lookup Table Power Macro-Models for Behavioral Library Components
VOLTA '99 Proceedings of the IEEE Alessandro Volta Memorial Workshop on Low-Power Design
Activity-sensitive architectural power analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Information theoretic measures for power analysis [logic design]
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Sequence compaction for power estimation: theory and practice
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Macro-models for high level area and power estimation on FPGAs
Proceedings of the 14th ACM Great Lakes symposium on VLSI
HyPE: hybrid power estimation for IP-based programmable systems
ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Methodology for high level estimation of FPGA power consumption
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
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In this paper, we present a novel sequence generator based on a Markov chain model. Specifically, we formulate the problem of generating a sequence of vectors with given average input probability p, average transition density d, and spatial correlation s as a transition matrix computation problem, in which the matrix elements are subject to constraints derived from the specified statistics. We also give a practical heuristic that computes such a matrix and generates a sequence of l n-bit vectors in O(nl + n2) time. Derived from a strongly mixing Markov chain, our generator yields binary vector sequences with accurate statistics, high uniformity, and high randomness. Experimental results show that our sequence generator can cover more than 99% of the parameter space. Sequences of 2,000 48-bit vectors are generated in less than 0.05 seconds, with average deviations of the signal statistics p, d, and s equal to 1.6%, 1.8%, and 2.8%, respectively.Our generator enables the detailed study of power macromodeling. Using our tool and the ISCAS-85 benchmark circuits, we have assessed the sensitivity of power dissipation to the three input statistics p, d, and s. Our investigation reveals that power is most sensitive to transition density, while only occasionally exhibiting high sensitivity to signal probability and spatial correlation. Our experiments also show that input signal imbalance can cause estimation errors as high as 100% in extreme cases, although errors are usually within 25%.