Ultra-low power neuromorphic computing with spin-torque devices

Mrigank Sharad, Deliang Fan, Karthik Yogendra, Kaushik Roy

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

3 Scopus citations

Abstract

Emerging spin transfer torque (ST) devices such as lateral spin valves and domain wall magnets may lead to ultra-low-voltage, current-mode, spin-torque switches that can offer attractive computing capabilities, beyond digital switches. This paper reviews our work on ST-based non-Boolean data-processing applications, like neural-networks, which involve analog processing. Integration of such spin-torque devices with charge-based devices like CMOS can lead to highly energy-efficient information processing hardware for applicatons like pattern-matching, neuromorphic-computing, image-processing and data-conversion. Simulation results for analog image processing and associative computing has shown the possibility of ∼100X improvement in energy efficiency as compared to a 15nm CMOS ASIC.

Original languageEnglish (US)
Title of host publication2013 3rd Berkeley Symposium on Energy Efficient Electronic Systems, E3S 2013 - Proceedings
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 3rd Berkeley Symposium on Energy Efficient Electronic Systems, E3S 2013 - Berkeley, CA, United States
Duration: Oct 28 2013Oct 29 2013

Publication series

Name2013 3rd Berkeley Symposium on Energy Efficient Electronic Systems, E3S 2013 - Proceedings

Conference

Conference2013 3rd Berkeley Symposium on Energy Efficient Electronic Systems, E3S 2013
CountryUnited States
CityBerkeley, CA
Period10/28/1310/29/13

Keywords

  • analog
  • interconnect
  • logic
  • low power
  • neural networks
  • non-Boolean
  • programmable logic array
  • spin
  • threshold logic

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology

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