Approximate computing with stochastic memristors

Rawan Naous; Khaled Nabil Salama

Approximate computing with stochastic memristors

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Approximate computing with memristive elements is a novel computing paradigm that sacrifices accuracy for power efficiency. It builds on the inherent stochastic switching behavior of the memristive devices to lower the voltage levels applied for the logic gates. With the added probabilistic operation of the underlying components, the accuracy of the output is to be studied and analyzed accordingly.

Original language	English (US)
Title of host publication	CNNA 2016 - 15th International Workshop on Cellular Nanoscale Networks and Their Applications
Editors	Ronald Tetzlaff
Publisher	IEEE Computer Society
Pages	97-98
Number of pages	2
ISBN (Electronic)	9783800742523
State	Published - 2016
Event	15th International Workshop on Cellular Nanoscale Networks and Their Applications, CNNA 2016 - Dresden, Germany Duration: Aug 23 2016 → Aug 25 2016

Publication series

Name	International Workshop on Cellular Nanoscale Networks and their Applications
Volume	2016-August
ISSN (Print)	2165-0160
ISSN (Electronic)	2165-0179

Conference

Conference	15th International Workshop on Cellular Nanoscale Networks and Their Applications, CNNA 2016
Country/Territory	Germany
City	Dresden
Period	08/23/16 → 08/25/16

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering

Cite this

@inproceedings{22fdf03bad8a4d9988834efd67063d10,

title = "Approximate computing with stochastic memristors",

abstract = "Approximate computing with memristive elements is a novel computing paradigm that sacrifices accuracy for power efficiency. It builds on the inherent stochastic switching behavior of the memristive devices to lower the voltage levels applied for the logic gates. With the added probabilistic operation of the underlying components, the accuracy of the output is to be studied and analyzed accordingly.",

author = "Rawan Naous and Salama, {Khaled Nabil}",

note = "Publisher Copyright: {\textcopyright} VDE VERLAG GMBH.; 15th International Workshop on Cellular Nanoscale Networks and Their Applications, CNNA 2016 ; Conference date: 23-08-2016 Through 25-08-2016",

year = "2016",

language = "English (US)",

series = "International Workshop on Cellular Nanoscale Networks and their Applications",

publisher = "IEEE Computer Society",

pages = "97--98",

editor = "Ronald Tetzlaff",

booktitle = "CNNA 2016 - 15th International Workshop on Cellular Nanoscale Networks and Their Applications",

address = "United States",

}

Naous, R & Salama, KN 2016, Approximate computing with stochastic memristors. in R Tetzlaff (ed.), CNNA 2016 - 15th International Workshop on Cellular Nanoscale Networks and Their Applications. International Workshop on Cellular Nanoscale Networks and their Applications, vol. 2016-August, IEEE Computer Society, pp. 97-98, 15th International Workshop on Cellular Nanoscale Networks and Their Applications, CNNA 2016, Dresden, Germany, 08/23/16.

Approximate computing with stochastic memristors. / Naous, Rawan; Salama, Khaled Nabil.
CNNA 2016 - 15th International Workshop on Cellular Nanoscale Networks and Their Applications. ed. / Ronald Tetzlaff. IEEE Computer Society, 2016. p. 97-98 (International Workshop on Cellular Nanoscale Networks and their Applications; Vol. 2016-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Naous, Rawan

AU - Salama, Khaled Nabil

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N2 - Approximate computing with memristive elements is a novel computing paradigm that sacrifices accuracy for power efficiency. It builds on the inherent stochastic switching behavior of the memristive devices to lower the voltage levels applied for the logic gates. With the added probabilistic operation of the underlying components, the accuracy of the output is to be studied and analyzed accordingly.

AB - Approximate computing with memristive elements is a novel computing paradigm that sacrifices accuracy for power efficiency. It builds on the inherent stochastic switching behavior of the memristive devices to lower the voltage levels applied for the logic gates. With the added probabilistic operation of the underlying components, the accuracy of the output is to be studied and analyzed accordingly.

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M3 - Conference contribution

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EP - 98

BT - CNNA 2016 - 15th International Workshop on Cellular Nanoscale Networks and Their Applications

A2 - Tetzlaff, Ronald

PB - IEEE Computer Society

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Y2 - 23 August 2016 through 25 August 2016

ER -

Approximate computing with stochastic memristors

Abstract

Publication series

Conference

ASJC Scopus subject areas

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