Abstract: A data analysis engine is implemented in a testbench to improve coverage convergence during simulation of a device-under-validation (DUV). During a first simulation phase initial stimulus data is generated according to initial random variables based on user-provided constraint parameters. The data analysis engine then uses a time-based technique to match coverage variables sampled from simulation response data with corresponding initial random variables, determines a functional dependency (relationship) between the sampled coverage variables and corresponding initial random variables, then automatically generates revised constraint parameters based on the functional dependency. The revised constraint parameters are then used during a second simulation phase to generate focused random variables used to stimulate the DUV to reach additional coverage variables. In one embodiment, the functional dependency is determined by cross-correlating sampled coverage variables and corresponding initial random variables.

Abstract: The present invention provides a graphical view of this connected network that allows the user to navigate throughout a network. The graph view consists of a series of nodes that correspond to a set of test, testbench, design or coverage items in the simulation. Various nodes in the network are colored or shaped differently to represent either test, class, stimulus, testbench, design or coverage points. The graph may be drawn so that all items that occur at the same time are lined up in the same horizontal or vertical region, to give the user an intuitive view of time going left to right or top to bottom.

Abstract: A data analysis engine is implemented in a testbench to improve coverage convergence during simulation of a device-under-validation (DUV). During a first simulation phase initial stimulus data is generated according to initial random variables based on user-provided constraint parameters. The data analysis engine then uses a time-based technique to match coverage variables sampled from simulation response data with corresponding initial random variables, determines a functional dependency (relationship) between the sampled coverage variables and corresponding initial random variables, then automatically generates revised constraint parameters based on the functional dependency. The revised constraint parameters are then used during a second simulation phase to generate focused random variables used to stimulate the DUV to reach additional coverage variables. In one embodiment, the functional dependency is determined by cross-correlating sampled coverage variables and corresponding initial random variables.

Abstract: The present invention provides a graphical view of this connected network that allows the user to navigate throughout a network. The graph view consists of a series of nodes that correspond to a set of test, testbench, design or coverage items in the simulation. Various nodes in the network are colored or shaped differently to represent either test, class, stimulus, testbench, design or coverage points. The graph may be drawn so that all items that occur at the same time are lined up in the same horizontal or vertical region, to give the user an intuitive view of time going left to right or top to bottom.

Abstract: The present invention provides a graphical view of this connected network that allows the user to navigate throughout a network. The graph view consists of a series of nodes that correspond to a set of test, testbench, design or coverage items in the simulation. Various nodes in the network are colored or shaped differently to represent either test, class, stimulus, testbench, design or coverage points. The graph may be drawn so that all items that occur at the same time are lined up in the same horizontal or vertical region, to give the user an intuitive view of time going left to right or top to bottom.

Abstract: The present invention provides a graphical view of this connected network that allows the user to navigate throughout a network. The graph view consists of a series of nodes that correspond to a set of test, testbench, design or coverage items in the simulation. Various nodes in the network are colored or shaped differently to represent either test, class, stimulus, testbench, design or coverage points. The graph may be drawn so that all items that occur at the same time are lined up in the same horizontal or vertical region, to give the user an intuitive view of time going left to right or top to bottom.