Memristor-based Network Switching Architecture for Energy Efficient Cognitive Computational Models

Saad Saleh, Boris Koldehofe

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

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Abstract

The Internet makes use of high performance network switches in order to route network traffic from end users to servers. Despite line-rate performance, the current switches consume huge energy and cannot support more expressive learning models, like cognitive functions using neuromorphic computations. The major reason is the use of transistors in the underlying Ternary Content-Addressable Memory (TCAM) which is volatile and supports digital computations only. These shortcomings can be bypassed by developing network memories building on novel components, like Memristors, due to their nonvolatile, nanoscale and analog storage/processing characteristics. In this paper, we propose the use of
a novel memristor-based Probabilistic Associative Memory, PAmM, which provides both digital (deterministic) and analog (probabilistic) outputs for supporting cognitive computational models in network switches. The traditional digital operations can be supported by a memristor-based energy efficient TCAM, called
TCAmMCogniGron. Building on PAmM and TCAmMCogniGron, we propose a novel network switching architecture and analyze its energy efficiency over the experimental dataset of a Nb-doped SrTiO3 memristive device. The results show that the proposed network switching architecture consumes only 0.01 fJ/bit/cell energy for analog compute operations which is at least 50 times less than the
digital operations.
Original languageEnglish
Title of host publicationThe 18th ACM International Symposium on Nanoscale Architectures (NANOARCH 2023)
Subtitle of host publicationProceedings
PublisherACM Press
Number of pages4
ISBN (Print)979-8-4007-0325-6
DOIs
Publication statusPublished - 18-Dec-2023
EventThe 18th ACM International Symposium on Nanoscale Architectures : (NANOARCH 2023) - Dresden, Germany
Duration: 18-Dec-202320-Dec-2023

Conference

ConferenceThe 18th ACM International Symposium on Nanoscale Architectures
Country/TerritoryGermany
CityDresden
Period18/12/202320/12/2023

Keywords

  • Memristors
  • TCAM
  • Switches
  • Cognitive Models
  • Energy Efficiency

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