Work

Fine-grained address segmentation for attention-based variable-degree prefetching

Year: 2022

Type: conference-paper

Abstract: Machine learning algorithms have shown potential to improve prefetching performance by accurately predicting future memory accesses. Existing approaches are based on the modeling of text prediction, considering prefetching as a classification problem for sequence prediction. However, the vast and sparse memory address space leads to large vocabulary, which makes this modeling impractical. The number and order of outputs for multiple cache line prefetching are also fundamentally different from text prediction.

Source: ACM International Conference on Computing Frontiers (CF)

DOI: 10.1145/3528416.3530236

Authors: Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori, Rajgopal Kannan, Viktor K. Prasanna

Institutions: University of Southern California, Intel (India), DEVCOM Army Research Laboratory, United States Army

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Air Force Research Laboratory

Awards: FoMR: DeepFetch: Compact Deep Learning based Prefetcher on Configurable Hardware, G8768854090