Advanced spintronic memory and logic for non-volatile processors

Robert Perricone, Ibrahim Ahmed, Zhaoxin Liang, Meghna G. Mankalale, X. Sharon Hu, Chris H. Kim, Michael Niemier, Sachin S. Sapatnekar, Jian Ping Wang

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

20 Scopus citations

Abstract

Many ultra-low power Internet of things (IoT) systems may be powered by energy harvested from ambient sources (e.g., solar radiation, thermal gradients, and WiFi). However, these energy sources can vary significantly in terms of their strengths and on/off patterns. For volatile systems, the intermittent nature of the energy sources necessitates the use of backup/recovery schemes to guarantee computational correctness and forward progress, which incur performance, area and energy overhead. Non-volatile (NV) processors based on spintronic devices, such as Spin-Transfer Torque (STT) memory and All-Spin-Logic (ASL), are more attractive alternatives. These NV devices are capable of achieving forward progress without relying on backup/recovery schemes. This work establishes a general framework for evaluating NV device-based processors for energy harvesting applications. Results demonstrate that NV spintronic processors can achieve significant energy savings (up to 83 x) versus a hybrid CMOS (computation) and STT-RAM (backup) implementation.

Original languageEnglish (US)
Title of host publicationProceedings of the 2017 Design, Automation and Test in Europe, DATE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages972-977
Number of pages6
ISBN (Electronic)9783981537093
DOIs
StatePublished - May 11 2017
Event20th Design, Automation and Test in Europe, DATE 2017 - Swisstech, Lausanne, Switzerland
Duration: Mar 27 2017Mar 31 2017

Publication series

NameProceedings of the 2017 Design, Automation and Test in Europe, DATE 2017

Other

Other20th Design, Automation and Test in Europe, DATE 2017
Country/TerritorySwitzerland
CitySwisstech, Lausanne
Period3/27/173/31/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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