Recurrent Neural Network-Based Constraint Alteration for PRG
Technique**Recurrent Neural Network-Based Constraint Alteration for PRG** is a simulation-based processor verification technique in which a recurrent neural network (RNN) dynamically changes the constraints of a pseudorandom generator (PRG) using coverage feedback from simulation. The approach was proposed for improving coverage closure in processor verification and for identifying a compact set of high-coverage stimuli suitable for regression testing.[994827fa-090a-491a-ad92-b89fa9ddbf00]
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Recurrent Neural Network-Based Constraint Alteration for PRG
Recurrent Neural Network-Based Constraint Alteration for PRG is a simulation-based processor verification technique in which a recurrent neural network (RNN) dynamically changes the constraints of a pseudorandom generator (PRG) using coverage feedback from simulation. The approach was proposed for improving coverage closure in processor verification and for identifying a compact set of high-coverage stimuli suitable for regression testing.[1]
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