Title :
Stimulus generation for constrained random simulation
Author :
Kitchen, Nathan ; Kuehlmann, Andreas
Author_Institution :
Univ. of California, Berkeley
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.
Keywords :
Markov processes; Monte Carlo methods; circuit simulation; formal verification; Markov-chain Monte Carlo method; constrained random simulation; efficient constraint solving; hardware verification flows; hybrid solver; mixed Boolean/integer variable domain; stimulus generation; Computer industry; Formal verification; Hardware; Hybrid power systems; Input variables; Runtime; Scalability; State-space methods; System testing;
Conference_Titel :
Computer-Aided Design, 2007. ICCAD 2007. IEEE/ACM International Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-1381-2
Electronic_ISBN :
1092-3152
DOI :
10.1109/ICCAD.2007.4397275
