Prefetching Using Principles of Hippocampal-Neocortical Interaction

Abstract

Memory prefetching improves performance across many systems layers. However, achieving high prefetch accuracy with low overhead is challenging, as memory hierarchies and application memory access patterns become more complicated. Furthermore, a prefetcher's ability to adapt to new access patterns as they emerge is becoming more crucial than ever. Recent work has demonstrated the use of deep learning techniques to improve prefetching accuracy, albeit with impractical compute and storage overheads. This paper suggests taking inspiration from the learning mechanisms and memory architecture of the human brain---specifically, the hippocampus and neocortex---to build resource-efficient, accurate, and adaptable prefetchers.