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
Online-Zugang:	Volltext
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