Scalable Auto-Tuning of Synthesis Parameters for Optimizing High-Performance Processors
Modern logic and physical synthesis tools provide numerous options and parameters that can drastically impact design quality; however, the large number of options leads to a complex design space difficult for human designers to navigate. By employing intelligent search strategies and parallel computing we can tackle this parameter tuning problem, thus automating one of the key design tasks conventionally performed by a human designer. In this paper we present a novel learning-based algorithm for synthesis parameter optimization. This new algorithm has been integrated into our existing autonomous parameter-tuning system, which was used to design multiple 22nm industrial chips and is currently being used for 14nm chips. These techniques show, on average, over 40% reduction in total negative slack and over 10% power reduction across hundreds of 14nm industrial processor macros while reducing overall human design effort. We also present a new higher-level system that manages parameter tuning of multiple designs in a scalable way. This new system addresses the needs of large design teams by prioritizing the tuning effort to maximize returns given the available compute resources.