Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.

Abstract: Methods and apparatuses are described for reducing or eliminating X-pessimism in gate-level simulation and/or formal verification. A system can identify a set of reconvergent inputs of a combinational block in a gate-level design. Next, the system can determine whether or not the combinational block is expected to exhibit X-pessimism during gate-level simulation. If the combinational block is expected to exhibit X-pessimism during gate-level simulation, the system can modify the gate-level design to reduce X-pessimism during gate-level simulation. In some embodiments, the system can build a model for the gate-level design by using unique free input variables to represent sources of indeterminate values. The system can then use the model to perform formal verification.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.

Abstract: One embodiment of the present invention provides a system that reuses information associated with a constraint solving operation for a problem domain. This system begins by receiving a constraint problem from the problem domain. Then, the system searches through a problem cache for an entry which corresponds to the canonical representation. If a corresponding entry does not exist in the problem cache, the system produces an entry in the problem cache for the canonical representation. Otherwise, if a corresponding entry already exists in the problem cache, the system generates a solution to the canonical representation by reusing the solver heuristic associated with the corresponding entry in the problem cache.

Abstract: Methods and apparatuses are described for reducing or eliminating X-pessimism in gate-level simulation and/or formal verification. A system can identify a set of reconvergent inputs of a combinational block in a gate-level design. Next, the system can determine whether or not the combinational block is expected to exhibit X-pessimism during gate-level simulation. If the combinational block is expected to exhibit X-pessimism during gate-level simulation, the system can modify the gate-level design to reduce X-pessimism during gate-level simulation. In some embodiments, the system can build a model for the gate-level design by using unique free input variables to represent sources of indeterminate values. The system can then use the model to perform formal verification.

Abstract: One embodiment of the present invention provides a system that reuses information associated with a constraint solving operation for a problem domain. This system begins by receiving a constraint problem from the problem domain. Then, the system searches through a problem cache for an entry which corresponds to the canonical representation. If a corresponding entry does not exist in the problem cache, the system produces an entry in the problem cache for the canonical representation. Otherwise, if a corresponding entry already exists in the problem cache, the system generates a solution to the canonical representation by reusing the solver heuristic associated with the corresponding entry in the problem cache.