The Interplay of Online and Offline Machine Learning for Industrial Design Flow Tuning

Modern logic and physical synthesis tools provide numerous options and parameters that can drastically affect design quality; however, the large number of options leads to a complex design space difficult for human designers to navigate. Fortunately, machine learning approaches and cloud computing environments are well suited for tackling complex parameter tuning problems like those seen in VLSI design flows. This talk proposes a holistic approach where online and offline machine learning approaches work together for tuning industrial design flows. We describe a system called SynTunSys (STS) that has been used to optimize multiple industrial high performance processors. STS consists of an online system that optimizes designs and generates data for a recommender system that performs offline training and recommendation. Experimental results show the collaboration between STS online and offline machine learning systems as well as insight from human designers provide best-of-breed results. Finally, we discuss potential new directions for research on design flow tuning.