Prefetching Using Principles of Hippocampal-Neocortical Interaction

Abstract

Memory prefetching improves performance across many systems layers, but achieving high accuracy with low overhead is increasingly difficult as memory hierarchies and application access patterns become more complicated. A prefetcher's ability to adapt to new access patterns is also becoming more important. Recent work has used deep learning to improve accuracy, albeit with impractical compute and storage costs. This paper proposes drawing on the learning mechanisms and memory architecture of the human brain, specifically the hippocampus and neocortex, to build resource-efficient, accurate, and adaptable prefetchers.

BibTeX

@inproceedings{cls,
  author = {Wu, Michael and Joshi, Ketaki and Sheinberg, Andrew and Cox, Guilherme and Khandelwal, Anurag and Pothukuchi, Raghavendra Pradyumna and Bhattacharjee, Abhishek},
  title = {{Prefetching Using Principles of Hippocampal-Neocortical Interaction}},
  year = {2023},
  mon = {June},
  booktitle = {ACM HotOS}
}