A Markov chain sequence generator for power macromodeling

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, i...

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Veröffentlicht in:Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design S. 404 - 411
Hauptverfasser: Liu, Xun, Papaefthymiou, Marios C.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 10.11.2002
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Applied sciences
Computing methodologies > Symbolic and algebraic manipulation > Symbolic and algebraic algorithms > Linear algebra algorithms
Design. Technologies. Operation analysis. Testing
Electronics
Exact sciences and technology
Hardware > Integrated circuits > Interconnect > Input > output circuits
Integrated circuits
Mathematics of computing > Probability and statistics > Probabilistic representations > Markov networks
Mathematics of computing > Probability and statistics > Stochastic processes > Markov processes
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
Estimation error
Circuit design
Error estimation
Input signal
Spatial correlation
Markov model
Markov chain
Matrix element
Transition matrix
Binary sequence
Energy dissipation
Heuristic method
Transition probability
Computer aided design
ISBN:0780376072, 9780780376076
ISSN:1092-3152
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Zusammenfassung: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%.
Bibliographie:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:0780376072
9780780376076
ISSN:1092-3152
DOI:10.1145/774572.774632