CausalTuner: Will Causality Help High-Dimensional EDA Tool Parameter Tuning

Electronic Design Automation (EDA) tools are central to Very Large Scale Integration (VLSI) design, where numerous parameters govern the Quality-of-Result (QoR) metrics, including performance, power, and area. The high dimensionality of the parameter space, coupled with complex interactions, makes manual tuning inefficient and hinders the scalability of automated methods. Existing methods typically treat parameters as flat vectors, neglecting the EDA flow’s hierarchical causal structure, where early-stage decisions constrain later downstream stages. To address this, we propose CausalTuner, a causality-aware design space exploration framework for efficient parameter tuning. It employs a hybrid causal attention mechanism to capture stage-wise parameter interactions and embeds them into deep kernel Gaussian processes for accurate and generalizable surrogate modeling. The causal exploration strategies enhance sampling efficiency. Experiments show that CausalTuner outperforms state-of-the-art methods in both final QoR and efficiency.