Yuanji Ye, M.Sc.

Prefetching techniques are widely used in digital systems to enhance performance. A prefetcher predicts and fetches data before it is actually accessed, thereby hiding memory access latency.

Traditional prefetchers typically consider only one program context and work well with regular memory access patterns. Recently, machine learning techniques such as neural networks and reinforcement learning have been employed in prefetcher design. These machine learning based prefetchers take into account more program and system-level information, allowing them to make smarter decisions. As a result, they often achieve higher accuracy, coverage, and timeliness, leading to improved system performance.

The goal of this seminar is to study and compare prefetching mechanisms based on different machine learning methodologies. After reading some papers, you should know the advantages of using machine learning in prefetching, as well as the challenges associated with its implementation. A starting point literature will be provided.

Prerequisites