Prof. Sheldon Tan received a three-year $500K award (CCF-2007135) from National Science Foundation for exploring data-driven and deep learning based approaches to addressing the VLSI reliability and robust chip design. Recently machine learning, especially deep learning is gaining much attention due to the breakthrough performance in various cognitive applications. Machine learning for electronic design automation (EDA) is also gaining significant traction as it provides new computing and optimization paradigms for many challenging design automation problems with complex nature.  The new project will leverage recent advance and breakthroughs in machine learning, especially deep learning to address the many challenging issues related to reliability,  resilience and robustness of the VLSI chips in the sub-7nm regime. 

More information about this new award can be found at NSF.