Applications of machine learning in EDA (Conference Presentation)
Paul D. Franzon · 2019
Machine learning has significant potential to help the human designer produce better outcomes. It can also help manage some of the complexity with chip design in advanced nodes. Projects to be discussed include the following. o Using Bayesian optimization for circuit IP reuse. Analog and custom digital IP reuse is difficult and time-consuming. Professor Franzon's group has built a Bayesian optimization approach involving statistical surrogate models. They have demonstrated by porting a number of designs between nodes, including a BJT to SOI port. The machine learning based approach produced better results than a human designer. o Using Surrogate modeling for Physical design. Professor Franzon's group has used a machine learning approach to predict Global Router and detailed router results as a function of tool input settings. These models can be used to tune the tool setup for specific outcomes. o Using Surrogate Modeling and System Identification Modeling to model digital receiver chains. Digital receiver modeling is predict BER of a receiver in the presence of an input signal with a close eye. Professor Franzon's group has used a combination of Surrogate Modeling and System Identification to build such a modeling capability. o Using deep networks for design rule checking. The team has demonstrated that deep networks can be used instead of Boolen checkers. There is significant potential to close the DFM and custom design productivity gaps.