DeepSwapper: A deep learning based page swap management scheme for hybrid memory systems

Majed Valad Beigi, Bahareh Pourshirazi, Gokhan Memik, Zhichun Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we introduce DeepSwapper, a deep learning-basedpage swap management scheme that utilizes RNN to performfast, energy-efficient, and temperature-aware page swapping inhybrid memory systems. DeepSwapper comprises of LSTM unitsof RNN model to predict the future memory accesses to guide itsswap management scheme, a dynamic page swap managementscheme that utilizes DRAM capacity efficiently by enabling hotpages in a swap group to be swapped with cold pages of anotherswap group, and a temperature-aware page swap managementscheme, which first predicts the future writes to NVM pages andthen, decides to migrate those pages with frequent writes in hotNVM banks to DRAM to enhance the NVM lifetime.

Original languageEnglish (US)
Title of host publicationPACT 2020 - Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages353-354
Number of pages2
ISBN (Electronic)9781450380751
DOIs
StatePublished - Sep 30 2020
Event2020 ACM International Conference on Parallel Architectures and Compilation Techniques, PACT 2020 - Virtual, Online, United States
Duration: Oct 3 2020Oct 7 2020

Publication series

NameParallel Architectures and Compilation Techniques - Conference Proceedings, PACT
ISSN (Print)1089-795X

Conference

Conference2020 ACM International Conference on Parallel Architectures and Compilation Techniques, PACT 2020
Country/TerritoryUnited States
CityVirtual, Online
Period10/3/2010/7/20

Keywords

  • Hybrid Main Memory
  • Lifetime
  • Performance
  • RNN
  • Temperature

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture

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