Energy-efficient non-boolean computing with spin neurons and resistive memory

Mrigank Sharad, Deliang Fan, Kyle Aitken, Kaushik Roy

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Emerging nonvolatile resistive memory technologies can be potentially suitable for computationally expensive analog pattern-matching tasks. However, the use of CMOS analog circuits with resistive crossbar memory (RCM) would result in large power consumption and poor scalability, thereby eschewing the benefits of RCM-based computation. We explore the potential of emerging spin-torque devices for RCM-based approximate computing circuits. Emerging spin-torque switching techniques may lead to nanoscale, current-mode spintronic switches that can be used for energy-efficient analog-mode data processing. We propose the use of such low-voltage, fast-switching, magnetometallic 'spin neurons' for ultralow 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 ∼100× lower power consumption.

Original languageEnglish (US)
Article number6637128
Pages (from-to)23-34
Number of pages12
JournalIEEE Transactions on Nanotechnology
Issue number1
StatePublished - Jan 2014
Externally publishedYes


  • Hardware
  • low power
  • magnets
  • memory
  • pattern matching

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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