A Learning-Based Recommender System for Autotuning Design Flows of Industrial High-Performance Processors
Jihye Kwon, Matthew M. Ziegler, Luca P. Carloni · 2019
Logic synthesis and physical design (LSPD) tools automate complex design tasks previously performed by human designers. One time-consuming task that remains manual is configuring the LSPD flow parameters, which significantly impacts design results. To reduce the parameter-tuning effort, we propose an LSPD parameter recommender system that involves learning a collaborative prediction model through tensor decomposition and regression. Using a model trained with archived data from multiple state-of-the-art 14nm processors, we reduce the exploration cost while achieving comparable design quality. Furthermore, we demonstrate the transfer-learning properties of our approach by showing that this model can be successfully applied for 7nm designs.