Tutorial: The Synergy of Hyperdimensional and In-Memory Computing

Paul R. Genssler, Simon Thomann, Hussam Amrouch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Breakthroughs in deep learning consistently drive innovation. However, DNN s tend to overwhelm conventional computing systems. Hyperdimensional Computing (HDC) is rapidly gaining prominence as a potent method for rapid learning from a relatively small amount of data. It also holds the promise of offering energy-efficient lightweight computation. This tutorial will provide a comprehensive overview of the major shortcomings of existing von Neumann architectures and the growing need for innovative designs that fundamentally reduce memory latency and energy consumption by enabling data processing within the memory itself. Additionally, the tutorial will delve into the immense potential of beyond-von Neumann architectures, which utilize both emerging beyond-CMOS devices like Ferroelectric Field-Effect Transistors (FeFET) and conventional CMOS devices.

Original languageEnglish
Title of host publicationProceedings - 2023 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-6
Number of pages2
ISBN (Electronic)9798400702891
DOIs
StatePublished - 2023
Event2023 ACM/IEEE International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2023 - Hamburg, Germany
Duration: 17 Sep 202322 Sep 2023

Publication series

NameProceedings - 2023 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2023

Conference

Conference2023 ACM/IEEE International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2023
Country/TerritoryGermany
CityHamburg
Period17/09/2322/09/23

Keywords

  • Hyperdimensional Computing
  • In-memory Computing
  • Machine Learning
  • Reliability

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