Aging And IR Drop Aware Power Mesh Prediction Based On Machine Learning

Scaling of process technology and the reduction of supply voltage are considered significant challenges for addressing IR drop and electromigration phenomena within the power supply network of integrated circuit. Conversely, disruptions in the power supply network contribute to the prevalent occurrence of aging mechanisms in sophisticated designs. Hence, having a reliable power supply network is a crucial aspect in the physical design of modern integrated circuits. Currently machine learning is a common method which is used in prediction algorithms, hence proposed algorithm uses machine learning for the prediction of power supply network architecture which will give more reliable power mesh for integrated circuits based on many physical design parameters, which are considered inputs for the proposed algorithm. The training data for the machine learning support is gathered from different power supply network architectures and designs with varying process sizes, gate counts, block sizes, floor plans, DCAP cell counts, and more. Experiments are also conducted for different Process, Voltage, and Temperature (PVT) conditions. Based on the collected data, distinct decision trees are created for predicting the appropriate power supply network architecture. The results indicate that the algorithm exhibits an approximate 13.57% inaccuracy rate using the currently implemented training data.