Method, product, and system for universal verification methodology (uvm) sequence selection using machine learning

Abstract

An approach is disclosed herein to sequence selection in a UVM environment. Generally, this approach includes a training phase for each machine learning model of a plurality of machine learning models. Each model is trained to achieve a particular target state and is rewarded when a selected action or sequence of actions causes movement that might be beneficial to achieving that target state. Once a respective model is trained, the trained model can then be used to determine which one action or sequence of actions (or ordered multiple thereof) to take to achieve the corresponding target state. Thus, by training and using a plurality of machine learning models to achieve a plurality of target states, and stimulating those machine learning models once trained, one or more actions and/or sequences of actions are generated as the selected sequences to be used to verify functionality or operation of a design under test.