Ultra low power associative computing with spin neurons and resistive crossbar memory

Mrigank Sharad, Deliang Fan, Kaushik Roy

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

41 Scopus citations

Abstract

Emerging resistive-crossbar memory (RCM) technology can be promising for computationally-expensive analog pattern-matching tasks. However, the use of CMOS analog-circuits with RCM would result in large power-consumption and poor scalability, thereby eschewing the benefits of RCM-based computation. We propose the use of low-voltage, fast-switching, magneto-metallic 'spin-neurons' for ultra low-power non-Boolean computing with RCM. We present the design of analog associative memory for face recognition using RCM, where, substituting conventional analog circuits with spin-neurons can achieve ̃100x lower power. This makes the proposed design ̃1000× more energy-efficient than a 45nm-CMOS digital ASIC, thereby significantly enhancing the prospects of RCM based computational hardware.

Original languageEnglish (US)
Title of host publicationProceedings of the 50th Annual Design Automation Conference, DAC 2013
DOIs
StatePublished - 2013
Externally publishedYes
Event50th Annual Design Automation Conference, DAC 2013 - Austin, TX, United States
Duration: May 29 2013Jun 7 2013

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Other

Other50th Annual Design Automation Conference, DAC 2013
CountryUnited States
CityAustin, TX
Period5/29/136/7/13

Keywords

  • Emerging circuits and devices
  • Magnets
  • Memory
  • Spin-transfer torque
  • Spintronics

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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