The Interplay of Online and Offline Machine Learning for 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 chapter proposes a holistic approach where online and offline machine learning approaches work together for tuning industrial design flows. We provide an overview of recent research on design flow tuning, spanning the application domains of high-level synthesis (HLS), field-programmable gate array (FPGA) synthesis and place-and-route, and VLSI logic synthesis and physical design (LSPD). We highlight the industrial design flow tuner SynTunSys (STS) as a case study. This system has been used to optimize multiple 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 provides best-of-breed results. Finally, we discuss potential new directions for design flow tuning research.