Efficient signal selection using supervised learning model for enhanced state restoration. (17th June 2020) - Rajendran, Agalya; Rajappa, Muthaiah
Abstract: The post‐silicon validation and debug is the most important task in the contemporary integrated circuit design methodology. The vital problem prevailing in this system is that it has limited observability and controllability due to the minimum number of storage space in the trace buffer. This tends to select the signals prudently in order to maximize state reconstruction. In the reported works, to select and to restore the signals efficiently it is categorized into two types like low simulation with high‐quality technique and high simulation with low‐quality technique. In this work, a node‐based combinational gate signal selection algorithm is proposed based on machine learning method that maximizes the state restoration capability. A significant improvement (80%) has made to achieve adequate simulation time with the high‐quality associated with the state‐of‐the‐art of supplementary methods.