Stimulus generation for constrained random simulation

Authors:
Nathan Kitchen;Andreas Kuehlmann
Affiliations:
University of California at Berkeley, CA;University of California at Berkeley, CA and Cadence Research Labs, Berkeley, CA
Venue:
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Year:
2007

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Abstract

Constrained random simulation is the main workhorse in today's hardware verification flows. It requires the random generation of input stimuli that obey a set of declaratively specified input constraints, which are then applied to validate given design properties by simulation. The efficiency of the overall flow depends critically on (1) the performance of the constraint solver and (2) the distribution of the generated solutions. In this paper we discuss the overall problem of efficient constraint solving for stimulus generation for mixed Boolean/integer variable domains and propose a new hybrid solver based on Markov-chain Monte Carlo methods with good performance and distribution.