Data Prefetching

Data prefetching aims to improve system performance by anticipating and retrieving data before it's explicitly requested, thereby reducing latency. Current research focuses on developing sophisticated prefetching strategies using machine learning models, such as graph neural networks, transformers, and deep reinforcement learning, to predict future memory accesses more accurately and efficiently across diverse applications and hardware architectures. These advancements are crucial for optimizing performance in various domains, including large-scale machine learning training, graph analytics, and video streaming, ultimately leading to faster and more energy-efficient systems.