Dependency preserving probabilistic modeling of switching activity using bayesian networks

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).

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INDEX TERMS

Primary Classification:
  B. Hardware
  B.7 INTEGRATED CIRCUITS
      B.7.1 Types and Design Styles
          Subjects: Advanced technologies

Additional Classification:
  B. Hardware
  B.8 Performance and Reliability

  J. Computer Applications
  J.2 PHYSICAL SCIENCES AND ENGINEERING
      Subjects: Electronics

General Terms:
Design, Experimentation, Measurement, Performance, Theory, Verification