February 10, 2018

Get Abstraction Refinement for Large Scale Model Checking PDF

By Chao Wang

ISBN-10: 0387341552

ISBN-13: 9780387341552

ISBN-10: 0387346007

ISBN-13: 9780387346007

Abstraction Refinement for big Scale version Checking summarizes contemporary study on abstraction concepts for version checking huge electronic approach. contemplating either the dimensions of present day electronic platforms and the capability of cutting-edge verification algorithms, abstraction is the single possible resolution for the profitable software of version checking options to industrial-scale designs. This ebook describes contemporary learn advancements in computerized abstraction refinement concepts. The suite of algorithms offered during this booklet has tested major development over previous artwork; a few of them have already been followed through the EDA businesses of their commercial/in-house verification instruments.

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The existence of a finite length counterexample is encoded as a Boolean formula, which is satisfiable if and only if the counterexample exists. The satisfiability problem of a Boolean formula can be decided by a SAT solver. Since modern SAT solvers [SS96, MMZ+01, GN02] often suffer less from the potential search space explosion, in practice, SAT based bounded model checking can handle some industrial-scale circuits that are beyond the reach of BDD based techniques. In bounded model checking, one can keep increasing the counterexample length k (also called the unrolling depth) until either a counterexample is found or k exceeds a predetermined completeness threshold.

X5 are the present-state variables. — =fT>) X3_^ \v^. 1. Illustration of fine-grain abstraction. Ay2 are also called the transition functions of Latch 1 and Latch 2, respectively.

M} be the a permutation of the indexes of the state variables; then, T{x,w,y) = f\Tj{x,w,yj) , where Tj{x^w^yj) is the bit transition relation of the j - t h binary state variable. j{x^w) be the next-state function of the j - t h state variable in terms of present-state variables and inputs, then Tj = (yj <-> Aj{x^w)). Note that Tj depends on one next-state variable yj but on potentially all present-state variables in x. Oyer-apgroximations of the two Boolean functions T and /, denoted by T and / , respectively, induce an^ abstract model that simulates A.

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Abstraction Refinement for Large Scale Model Checking by Chao Wang


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