Unveiling the Black-Box: Leveraging Explainable AI for FPGA Design Space Optimization

With significant advancements in various design methodologies, modern integrated circuits have experienced noteworthy improvements in power, performance, and area. Among various methodologies, design space optimization (DSO), which automatically explores electronic design automation (EDA) tool parameters for a given design, has been extensively studied in recent years. In this study, we propose an approach to fine-tuning an effective FPGA design space to suit a specific design. By utilizing our ML-based prediction and explainable artificial intelligence (XAI) approach, we quantify parameter contribution scores, which reveal the correlation between each parameter and the final timing results. Using the valuable insights from the parameter contribution scores, we can refine the design space only with effective parameters for subsequent timing optimization. During the optimization, our framework improved the maximum operating frequency by 26% on average in six test designs. To accomplish this, our framework required even 47% fewer FPGA compilations than the baseline, demonstrating its superior capacity for achieving fast convergence.