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Efficient signal selection using supervised learning model for enhanced state restoration
Author(s) -
Rajendran Agalya,
Rajappa Muthaiah
Publication year - 2021
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12344
Subject(s) - observability , computer science , state (computer science) , trace (psycholinguistics) , controllability , selection (genetic algorithm) , signal (programming language) , debugging , task (project management) , quality (philosophy) , artificial intelligence , computer engineering , machine learning , algorithm , engineering , mathematics , programming language , linguistics , philosophy , systems engineering , epistemology
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.