Automatic IR-Drop ECO Using Machine Learning

Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies

“…If all instances are considered simultaneously, the design matrix is too large. In order to mitigate the training scale, many researchers are inclined to partition the floorplan and then select parts of IRD critical partitions to construct a training model, such as in [10][11][12][13]. This method can really save training time, but the cell IRD in other unselected partitions is not completely considered.…”

“…Convolutional neural network (CNN) is a widespread machine learning model used in many research fields, especially image and voice recognition, and it was used for IR-drop prediction as well [9,10] because an IR-drop map can be expressed as a figure. Regression tree is another widespread used machine learning model for many industrial application fields and it was also used for IR-drop estimation [10][11][12][13]. XGBoost [14], a gradient boosting regression tree, was proposed in 2016, which is proved to be behaving perfectly in most non-image application fields.…”

“…XGBoost [14], a gradient boosting regression tree, was proposed in 2016, which is proved to be behaving perfectly in most non-image application fields. In addition, XGBoost has achieved amazing results compared with other decision trees or even with some deep learning algorithms [10,15], and it has been used for IR-drop prediction as well [11][12][13]. A major weakness shared by most of the previous works was that they focused on vector-based analysis but ignored vectorless IR-drop.…”