PSS action sequence modeling using Machine Learning

In general, one of the most difficult aspects of ML is collecting data for learning. This is because the more learn-ing data, the higher the prediction accuracy of ML. In this regard, we expected that the combination of PSS, which can easily generate numerous tests, and ML, which finds regularity based on collected data, could exert fantastic synergy. And we were able to dramatically increase the verification coverage by being able to freely create concur-rent function events that were previously considered impossible at the simulation level through PSS and ML

Moonki Jang, Samsung Electronics
Myeongwhan Hyun, Samsung Electronics
Hyunkyu Ahn, Samsung Electronics
Jiwoong Kim, Samsung Electronics
Yunwhan Kim, Samsung Electronics
Dongjoo Kim, Samsung Electronics