A Markov chain sequence generator for power macromodeling

• Published in: Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design pp. 404 - 411
• Main Authors: Liu, Xun, Papaefthymiou, Marios C.
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 10.11.2002; IEEE
• Series: ACM Conferences
• Subjects: 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
• DOI: 10.1145/774572.774632

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%.