Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases

Geraldo F. Oliveira, Amirali Boroumand, Saugata Ghose, Juan Gomez-Luna, Onur Mutlu

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

Abstract

Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take place on the data. Unfortunately, this data movement is a major bottleneck for system performance and energy consumption [1], [2]. One promising execution paradigm that alleviates the data movement bottleneck in modern and emerging applications is processing-in-memory (PIM) [2]-[12], where the cost of data movement to/from main memory is reduced by placing computation capabilities close to memory. In the data-centric PIM paradigm, the logic close to memory has access to data with significantly higher memory bandwidth, lower latency, and lower energy consumption than processors/accelerators in existing processor-centric systems.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2022
PublisherIEEE Computer Society
Pages273-278
Number of pages6
ISBN (Electronic)9781665466059
DOIs
StatePublished - 2022
Event2022 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2022 - Pafos, Cyprus
Duration: Jul 4 2022Jul 6 2022

Publication series

NameProceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
Volume2022-July
ISSN (Print)2159-3469
ISSN (Electronic)2159-3477

Conference

Conference2022 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2022
Country/TerritoryCyprus
CityPafos
Period7/4/227/6/22

Keywords

  • accelerator
  • databases
  • machine learning
  • neural networks
  • processing in memory

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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