Abstract: Methods, systems, and devices for tuning a set of simulation parameters associated with a design verification environment are described that include: simulating a circuit design according to a set of simulation parameters; providing, to a machine learning network, an indication of functional coverage results associated with simulating the circuit design according to the set of simulation parameters; receiving an output in response to the machine learning network processing the functional coverage results; and simulating the circuit design based on a recommended set of simulation parameters, wherein simulating the circuit design based on the recommended set of simulation parameters includes generating a set of component signals associated with satisfying a target functional coverage statement. In some aspects, the output includes: the target functional coverage statement associated with the circuit design; and the recommended set of the simulation parameters.

Abstract: Methods, systems, and devices for tuning a set of simulation parameters associated with a design verification environment are described that include: simulating a circuit design according to a set of simulation runs; providing, to a machine learning network, an indication of functional coverage results associated with simulating the circuit design according to the set of simulation runs; receiving an output in response to the machine learning network processing the functional coverage results; and simulating the circuit design based on a recommended set of simulation parameters. The output includes: an indication of a near miss event associated with the functional coverage results; and the recommended set of simulation parameters. Simulating the circuit design based on the recommended set of simulation parameters includes generating a set of component signals associated with triggering the near miss event.