An offline frequent value encoding for energy-efficient MLC/TLC non-volatile memories

Ali Alsuwaiyan; Kartik Mohanram

doi:10.1145/2902961.2902979

An offline frequent value encoding for energy-efficient MLC/TLC non-volatile memories

Ali Alsuwaiyan, Kartik Mohanram

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

3 Scopus citations

Abstract

This paper describes a low overhead, offline frequent value encoding (FVE) solution to reduce the write energy in multi-level/triple-level cell (MLC/TLC) non-volatile memories (NVMs). The proposed solution, which does not require any runtime software support, clusters a set of general-purpose applications according to their data frequency profiles and generates a dedicated offline FVE that minimizes write energy for each cluster. Results show that the write energy reduction of evaluation sets - using FVEs generated for training sets - are close (equal) to the best known solution for MLC (TLC) NVM encoding; however, our solution incurs a memory overhead that is 16× (5.7×) less than the best comparable scheme in the literature for MLC (TLC) NVMs.

Original language	English
Title of host publication	GLSVLSI 2016 - Proceedings of the 2016 ACM Great Lakes Symposium on VLSI
Publisher	Association for Computing Machinery
Pages	403-408
Number of pages	6
ISBN (Electronic)	9781450342742
DOIs	https://doi.org/10.1145/2902961.2902979
State	Published - 18 May 2016
Externally published	Yes

Publication series

Name	Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
Volume	18-20-May-2016

Bibliographical note

Publisher Copyright:
© 2016 ACM.

Keywords

Encoding
Energy
Non-volatile memories

ASJC Scopus subject areas

Engineering (all)

Access to Document

10.1145/2902961.2902979

Cite this

Alsuwaiyan, A., & Mohanram, K. (2016). An offline frequent value encoding for energy-efficient MLC/TLC non-volatile memories. In GLSVLSI 2016 - Proceedings of the 2016 ACM Great Lakes Symposium on VLSI (pp. 403-408). (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI; Vol. 18-20-May-2016). Association for Computing Machinery. https://doi.org/10.1145/2902961.2902979

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abstract = "This paper describes a low overhead, offline frequent value encoding (FVE) solution to reduce the write energy in multi-level/triple-level cell (MLC/TLC) non-volatile memories (NVMs). The proposed solution, which does not require any runtime software support, clusters a set of general-purpose applications according to their data frequency profiles and generates a dedicated offline FVE that minimizes write energy for each cluster. Results show that the write energy reduction of evaluation sets - using FVEs generated for training sets - are close (equal) to the best known solution for MLC (TLC) NVM encoding; however, our solution incurs a memory overhead that is 16× (5.7×) less than the best comparable scheme in the literature for MLC (TLC) NVMs.",

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author = "Ali Alsuwaiyan and Kartik Mohanram",

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year = "2016",

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doi = "10.1145/2902961.2902979",

language = "English",

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Alsuwaiyan, A & Mohanram, K 2016, An offline frequent value encoding for energy-efficient MLC/TLC non-volatile memories. in GLSVLSI 2016 - Proceedings of the 2016 ACM Great Lakes Symposium on VLSI. Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI, vol. 18-20-May-2016, Association for Computing Machinery, pp. 403-408. https://doi.org/10.1145/2902961.2902979

An offline frequent value encoding for energy-efficient MLC/TLC non-volatile memories. / Alsuwaiyan, Ali; Mohanram, Kartik.
GLSVLSI 2016 - Proceedings of the 2016 ACM Great Lakes Symposium on VLSI. Association for Computing Machinery, 2016. p. 403-408 (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI; Vol. 18-20-May-2016).

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

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N2 - This paper describes a low overhead, offline frequent value encoding (FVE) solution to reduce the write energy in multi-level/triple-level cell (MLC/TLC) non-volatile memories (NVMs). The proposed solution, which does not require any runtime software support, clusters a set of general-purpose applications according to their data frequency profiles and generates a dedicated offline FVE that minimizes write energy for each cluster. Results show that the write energy reduction of evaluation sets - using FVEs generated for training sets - are close (equal) to the best known solution for MLC (TLC) NVM encoding; however, our solution incurs a memory overhead that is 16× (5.7×) less than the best comparable scheme in the literature for MLC (TLC) NVMs.

AB - This paper describes a low overhead, offline frequent value encoding (FVE) solution to reduce the write energy in multi-level/triple-level cell (MLC/TLC) non-volatile memories (NVMs). The proposed solution, which does not require any runtime software support, clusters a set of general-purpose applications according to their data frequency profiles and generates a dedicated offline FVE that minimizes write energy for each cluster. Results show that the write energy reduction of evaluation sets - using FVEs generated for training sets - are close (equal) to the best known solution for MLC (TLC) NVM encoding; however, our solution incurs a memory overhead that is 16× (5.7×) less than the best comparable scheme in the literature for MLC (TLC) NVMs.

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An offline frequent value encoding for energy-efficient MLC/TLC non-volatile memories

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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