A fast parameter tuning framework via transfer learning and multi-objective bayesian optimization

Abstract

Design space exploration (DSE) can automatically and effectively determine design parameters to achieve the optimal performance, power and area (PPA) in very large-scale integration (VLSI) design. The lack of prior knowledge causes low efficient exploration. In this paper, a fast parameter tuning framework via transfer learning and multi-objective Bayesian optimization is proposed to quickly find the optimal design parameters. Gaussian Copula is utilized to establish the correlation of the implemented technology. The prior knowledge is integrated into multi-objective Bayesian optimization through transforming the PPA data to residual observation. The uncertainty-aware search acquisition function is employed to explore design space efficiently. Experiments on a CPU design show that this framework can achieve a higher quality of Pareto frontier with less design flow running than state-of-the-art methodologies.