Attention-Based EDA Tool Parameter Explorer: From Hybrid Parameters to Multi-QoR Metrics

Improving the outcomes of very-large-scale integration design without altering the underlying design enablement, such as process, device, interconnect, and IPs, is critical for integrated circuit (IC) designers. Parameter tuning for electronic design automation (EDA) tools is an emerging technology for improving the final design Quality-of-Result (QoR). It can be observed that many complex heuristics have been accreted upon previous complex heuristics integrated into tools, resulting in a vast number of tunable parameters. Even worse, these parameters include both continuous and discrete ones, making the parameter tuning process laborious and challenging. In this paper, we propose an attention-based EDA tool parameter explorer. A self-attention mechanism is developed to navigate the parameter importance. A hybrid space Gaussian process model is leveraged to optimize continuous and discrete parameters jointly, capturing their complex interactions. Considering multiple QoR metrics and the large amount of time required to invoke EDA tools, a customized acquisition function based on expected hypervolume improvement (EHVI) is proposed to enable multi-objective optimization and parallel evaluation. In addition, a self-adjusting additive kernel is proposed to optimize the hybrid space Bayesian Optimization flow and increase its explainability. Experimental results on a set of IWLS2005 benchmarks demonstrate the effectiveness and efficiency of our method.