Uncovering the Intricacies and Synergies of Processor Microarchitecture Mechanisms Using Explainable AI

This paper defines a data-driven methodology seamlessly combining machine learning (ML) and eXplainable Artificial Intelligence (XAI) techniques to address the challenge of understanding the intricate relationships between microarchitecture mechanisms with respect to system performance. By applying the SHapley Additive exPlanations (SHAP) XAI method, it analyzes the synergies of cache replacement, branch prediction, and hardware prefetching on instructions per cycle (IPC) scores. We validate our methodology by using the SPEC CPU 2006 and 2017 benchmark suites with the ChampSim simulator. We illustrate the benefits of the proposed methodology and discuss the major insights and limitations obtained from this study.