Dependency preserving probabilistic modeling of switching activity using bayesian networks

  • Authors:
  • Sanjukta Bhanja;N. Ranganathan

  • Affiliations:
  • Dept. of Computer Science and Engineering, Center for Microelectronics Research, University of South Florida, Tampa, Florida;Dept. of Computer Science and Engineering, Center for Microelectronics Research, University of South Florida, Tampa, Florida

  • Venue:
  • Proceedings of the 38th annual Design Automation Conference
  • Year:
  • 2001

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Abstract

We propose a new switching probability model for combinational circuits using aLogic-Induced-Directed-Acyclic-Graph(LIDAG) and prove that such a graph corresponds to aBayesian Networkguaranteed to map all the dependencies inherent in the circuit. This switching activity can be estimated by capturing complex dependencies (spatio-temporal and conditional) among signals efficiently by local message-passing based on the Bayesian networks. Switching activity estimation of ISCAS and MCNC circuits with random input streams yield high accuracy (average mean error=0.002) and low computational time (average time=3.93 seconds).