Fine-Grained Mapping of AI Training & Inference Workloads Across the Full Memory Hierarchy

Understanding this behavior modern AI training and inference workloads is crucial for designing optimized memory hierarchies and minimizing data‑movement bottlenecks. In this internship, the student will develop a fine‑grained workload‑to‑memory mapping framework that characterizes how representative AI workloads interact with the entire memory stack. This includes profiling layer‑level and operator‑level footprints, traffic patterns, reuse distances, and temporal/spatial locality. By integrating these insights into a system‑level simulation environment, the student will evaluate bottlenecks, identify optimization opportunities, and generate actionable guidelines for future memory hierarchy design targeting large‑scale AI systems.