In-memory hamming similarity computation in resistive arrays

Yuval Cassuto; Koby Crammer

doi:10.1109/ISIT.2015.7282569

In-memory hamming similarity computation in resistive arrays

Yuval Cassuto
, Koby Crammer

Technion - Israel Institute of Technology

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

Abstract

This paper develops a framework to calculate Hamming similarity between vectors stored in resistive memory. A single-parameter model is proposed for the resistive measurement channel, which is then used to analytically reveal an interesting tradeoff between this parameter and succeeding in the calculation task. We suggest coding techniques that can improve this tradeoff under natural usage assumptions. The proposed constructions work to change the Hamming weight of the stored vectors without corrupting the Hamming distance between pairs of vectors.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015
Pages	819-823
Number of pages	5
ISBN (Electronic)	9781467377041
DOIs	https://doi.org/10.1109/ISIT.2015.7282569
State	Published - 28 Sep 2015
Event	2015 IEEE International Symposium on Information Theory, ISIT 2015 - Hong Kong, China Duration: 14 Jun 2015 → 19 Jun 2015

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
Volume	2015-June

Conference

Conference	2015 IEEE International Symposium on Information Theory, ISIT 2015
Country/Territory	China
City	Hong Kong
Period	14/06/15 → 19/06/15

ASJC Scopus subject areas

Theoretical Computer Science
Information Systems
Modelling and Simulation
Applied Mathematics

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10.1109/ISIT.2015.7282569

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title = "In-memory hamming similarity computation in resistive arrays",

abstract = "This paper develops a framework to calculate Hamming similarity between vectors stored in resistive memory. A single-parameter model is proposed for the resistive measurement channel, which is then used to analytically reveal an interesting tradeoff between this parameter and succeeding in the calculation task. We suggest coding techniques that can improve this tradeoff under natural usage assumptions. The proposed constructions work to change the Hamming weight of the stored vectors without corrupting the Hamming distance between pairs of vectors.",

author = "Yuval Cassuto and Koby Crammer",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Symposium on Information Theory, ISIT 2015 ; Conference date: 14-06-2015 Through 19-06-2015",

year = "2015",

month = sep,

day = "28",

doi = "10.1109/ISIT.2015.7282569",

language = "English",

series = "IEEE International Symposium on Information Theory - Proceedings",

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Cassuto, Y & Crammer, K 2015, In-memory hamming similarity computation in resistive arrays. in Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015., 7282569, IEEE International Symposium on Information Theory - Proceedings, vol. 2015-June, pp. 819-823, 2015 IEEE International Symposium on Information Theory, ISIT 2015, Hong Kong, China, 14/06/15. https://doi.org/10.1109/ISIT.2015.7282569

In-memory hamming similarity computation in resistive arrays. / Cassuto, Yuval; Crammer, Koby.
Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015. 2015. p. 819-823 7282569 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2015-June).

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

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N2 - This paper develops a framework to calculate Hamming similarity between vectors stored in resistive memory. A single-parameter model is proposed for the resistive measurement channel, which is then used to analytically reveal an interesting tradeoff between this parameter and succeeding in the calculation task. We suggest coding techniques that can improve this tradeoff under natural usage assumptions. The proposed constructions work to change the Hamming weight of the stored vectors without corrupting the Hamming distance between pairs of vectors.

AB - This paper develops a framework to calculate Hamming similarity between vectors stored in resistive memory. A single-parameter model is proposed for the resistive measurement channel, which is then used to analytically reveal an interesting tradeoff between this parameter and succeeding in the calculation task. We suggest coding techniques that can improve this tradeoff under natural usage assumptions. The proposed constructions work to change the Hamming weight of the stored vectors without corrupting the Hamming distance between pairs of vectors.

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DO - 10.1109/ISIT.2015.7282569

M3 - Conference contribution

T3 - IEEE International Symposium on Information Theory - Proceedings

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BT - Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015

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ER -

In-memory hamming similarity computation in resistive arrays

Abstract

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