Multi-Layer In-Memory Processing

Daichi Fujiki, Alireza Khadem, Scott Mahlke, Reetuparna Das

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

Abstract

In-memory computing provides revolutionary changes to computer architecture by fusing memory and computation, allowing data-intensive computations to reduce data communications. Despite promising results of in-memory computing in each layer of the memory hierarchy, an integrated approach to a system with multiple computable memories has not been examined. This paper presents a holistic and application-driven approach to building Multi-Layer In-Memory Processing (MLIMP) systems, enabling applications with variable computation demands to reap the benefits of heterogeneous compute resources in an integrated MLIMP system. By introducing concurrent task scheduling to MLIMP, we achieve improved performance and energy efficiency for graph neural networks and multiprogramming of data parallel applications.

Original languageEnglish
Title of host publicationProceedings - 2022 55th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2022
PublisherIEEE Computer Society
Pages920-936
Number of pages17
ISBN (Electronic)9781665462723
DOIs
Publication statusPublished - 2022
Event55th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2022 - Chicago, United States
Duration: 2022 Oct 12022 Oct 5

Publication series

NameProceedings of the Annual International Symposium on Microarchitecture, MICRO
Volume2022-October
ISSN (Print)1072-4451

Conference

Conference55th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2022
Country/TerritoryUnited States
CityChicago
Period22/10/122/10/5

Keywords

  • accelerator
  • GNN
  • in-memory computing
  • processing in memory

ASJC Scopus subject areas

  • Hardware and Architecture

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