Synopsys, Inc.

Mary Lynn Meyer

Won Sub Kim

Daniel Marcos Chapiro

Capabilities are added to a Hardware Verification Language that facilitates the generation of test data. Random number sources, called random variables, can be produced by adding a randomness attribute to a variable declaration of a class definition. A 'randomize' method call to a class instance produces a random value for each random variable. Constraint blocks, of a class definition, control random variables with constraint expressions. Dependency, of random variable value assignment, as determined by constraint expressions, can be expressed by a DAG. A constraint expression is converted into ranges of permissible values, from which a value is randomly chosen by a randomize method. A 'boundary' method call sequentially selects a combination of boundary values, for each random variable, from each random variable's set of ranges. Coordinated selection of a boundary values permits all combinations of boundary values to be produced through successive boundary calls.

Method and apparatus for random stimulus generation

The present invention adds capabilities to a Hardware Verification Language (HVL) which facilitate the generation of random test data. Sources of random numbers are easily produced by simply adding a randomness attribute to a variable declaration of a class definition. Such variables are called random variables. A &ldquo;randomize&rdquo; method call may be made to an instance of the class definition to produce random values for each random variable. The values assigned to random variables are controlled using constraint blocks, which are part of the class definition. A constraint block is comprised of constraint expressions. The constraint expressions may impose a linear ordering in which random variable values must be assigned and this dependency is expressed by directed acyclic graphs (DAGs). The constraint expressions constraining each random variable are converted into ranges of permissible values from which a value is chosen at random. Rather than selecting a value for each random variable from within one of its ranges, as done for the randomize method, each boundary method call sequentially selects a combination of boundary values, for each random variable, from each random variable's set of ranges. By selecting a boundary value for each random variable of the instance, in a coordinated fashion, all combinations of boundary values can be produced through successive calls to the boundary function. As with the randomize method, since random variables may depend upon each other, the selection of a boundary value for one random variable may change the selection of boundary values available for other random variables.

The present invention adds capabilities to a Hardware Verification Language (HVL) which facilitate the generation of random test data. Sources of random numbers are easily produced by simply adding a randomness attribute to a variable declaration of a class definition. Such variables are called random variables. A &ldquo;randomize&rdquo; method call may be made to an instance of the class definition to produce random values for each random variable. The values assigned to random variables are controlled using constraint blocks, which are part of the class definition. A constraint block is comprised of constraint expressions, where each constraint expression limits the values that can be assigned to a random variable on the left-hand-side (lhs) of the constraint expression. If a constraint block of an instance is active or ON, then all the constraint expressions in the block will act to constrain their lhs random variable. A constraint block which is OFF means that all of its constraint expressions will not act to constrain their random variables. The method &ldquo;constraint_mode&rdquo; can be used to turn ON or OFF any constraint blocks of an instance.

The present invention adds capabilities to a Hardware Verification Language (HVL) which facilitate the generation of random test data. Sources of random numbers are easily produced by simply adding a randomness attribute to a variable declaration of a class definition. Such variables are called random variables. A &ldquo;randomize&rdquo; method call may be made to an instance of the class definition to produce random values for each random variable. The values assigned to random variables are controlled using constraint blocks, which are part of the class definition. A constraint block is comprised of constraint expressions, where each constraint expression limits the values that can be assigned to a random variable on the left-hand-side (lhs) of the constraint expression. A constraint_expression can constrain any random variable which has been declared at its level in the class hierarchy, or at any higher level. A constraint_expression cannot constrain a random variable declared at a lower level in the hierarchy. If a constraint block of an instance is active or ON, then all the constraint expressions in the block will act to constrain their lhs random variable. A constraint block which is OFF means that all of its constraint expressions will not act to constrain their random variables. The method &ldquo;constraint_mode&rdquo; can be used to turn ON or OFF any constraint blocks of an instance. The effects of a constraint block can be completely eliminated by defining, at a lower level in the hierarchy, another constraint block with the same name.

The present invention adds capabilities to a Hardware Verification Language (HVL) which facilitate the generation of random test data. Sources of random numbers are easily produced by simply adding a randomness attribute to a variable declaration of a class definition. Such variables are called random variables. A &ldquo;randomize&rdquo; method call may be made to an instance of the class definition to produce random values for each random variable. The values assigned to random variables are controlled using constraint blocks, which are part of the class definition. A constraint block is comprised of constraint expressions, where each constraint expression limits the values that can be assigned to a random variable on the left-hand-side (lhs) of the constraint expression. Because random variables may also appear on the right-hand-side (rhs) of a constraint expression there is an ordering in which random variable values must be assigned and this dependency is expressed by directed acyclic graphs (DAGs). A linear ordering for assigning values to the random variables is derived from the DAGs. The constraint expressions constraining each random variable are converted into ranges of permissible values from which a value is chosen at random.

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Mountain View, CA, United States of America

Mary Lynn Meyer