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Guest Editorial Memristive-Device-Based Computing. / Hamdioui, Said; Gaillardon, Pierre Emmanuel; Fey, Dietmar; Simunic Rosing, Tajana.

In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 26, No. 12, 8554356, 2018, p. 2581-2583.

Research output: Contribution to journalEditorialScientific

Harvard

Hamdioui, S, Gaillardon, PE, Fey, D & Simunic Rosing, T 2018, 'Guest Editorial Memristive-Device-Based Computing' IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 26, no. 12, 8554356, pp. 2581-2583. https://doi.org/10.1109/TVLSI.2018.2878679

APA

Hamdioui, S., Gaillardon, P. E., Fey, D., & Simunic Rosing, T. (2018). Guest Editorial Memristive-Device-Based Computing. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 26(12), 2581-2583. [8554356]. https://doi.org/10.1109/TVLSI.2018.2878679

Vancouver

Hamdioui S, Gaillardon PE, Fey D, Simunic Rosing T. Guest Editorial Memristive-Device-Based Computing. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 2018;26(12):2581-2583. 8554356. https://doi.org/10.1109/TVLSI.2018.2878679

Author

Hamdioui, Said ; Gaillardon, Pierre Emmanuel ; Fey, Dietmar ; Simunic Rosing, Tajana. / Guest Editorial Memristive-Device-Based Computing. In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 2018 ; Vol. 26, No. 12. pp. 2581-2583.

BibTeX

@article{1d45098b6e71497fb1852d5b56d830b0,
title = "Guest Editorial Memristive-Device-Based Computing",
abstract = "Today’s and emerging computing tasks are extremely demanding in terms of storage, energy efficiency, and computing efficiency; data-intensive/big-data applications and Internet-of-Things are couple of examples. In addition, today’s computer architectures and device technologies are facing major challenges making them incapable to deliver the required functionalities and features. Computers are facing the three well-known walls [1)] : the memory wall, the instruction level parallelism wall, and the power wall. Similarly, nanoscale CMOS technology is facing three walls [2)] : the reliability wall, the leakage wall, and the cost wall. In order for computing systems to continue to deliver sustainable benefits for the foreseeable future society, alternative computing architectures have to be explored in the light of emerging new device technologies. Using memristive device technology [3)] to enable new computing paradigms such as computation-in-memory architecture [4)] – [7)] is one of the emerging alternatives that could provide a huge potential in terms of energy and computing efficiency.",
keywords = "Special issues and sections, Memristors, Logic circuits, Design automation, Hardware, Nonvolatile memory, Integrated circuit interconnections, Computer architecture",
author = "Said Hamdioui and Gaillardon, {Pierre Emmanuel} and Dietmar Fey and {Simunic Rosing}, Tajana",
year = "2018",
doi = "10.1109/TVLSI.2018.2878679",
language = "English",
volume = "26",
pages = "2581--2583",
journal = "IEEE Transactions on Very Large Scale Integration (VLSI) Systems",
issn = "1063-8210",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "12",

}

RIS

TY - JOUR

T1 - Guest Editorial Memristive-Device-Based Computing

AU - Hamdioui, Said

AU - Gaillardon, Pierre Emmanuel

AU - Fey, Dietmar

AU - Simunic Rosing, Tajana

PY - 2018

Y1 - 2018

N2 - Today’s and emerging computing tasks are extremely demanding in terms of storage, energy efficiency, and computing efficiency; data-intensive/big-data applications and Internet-of-Things are couple of examples. In addition, today’s computer architectures and device technologies are facing major challenges making them incapable to deliver the required functionalities and features. Computers are facing the three well-known walls [1)] : the memory wall, the instruction level parallelism wall, and the power wall. Similarly, nanoscale CMOS technology is facing three walls [2)] : the reliability wall, the leakage wall, and the cost wall. In order for computing systems to continue to deliver sustainable benefits for the foreseeable future society, alternative computing architectures have to be explored in the light of emerging new device technologies. Using memristive device technology [3)] to enable new computing paradigms such as computation-in-memory architecture [4)] – [7)] is one of the emerging alternatives that could provide a huge potential in terms of energy and computing efficiency.

AB - Today’s and emerging computing tasks are extremely demanding in terms of storage, energy efficiency, and computing efficiency; data-intensive/big-data applications and Internet-of-Things are couple of examples. In addition, today’s computer architectures and device technologies are facing major challenges making them incapable to deliver the required functionalities and features. Computers are facing the three well-known walls [1)] : the memory wall, the instruction level parallelism wall, and the power wall. Similarly, nanoscale CMOS technology is facing three walls [2)] : the reliability wall, the leakage wall, and the cost wall. In order for computing systems to continue to deliver sustainable benefits for the foreseeable future society, alternative computing architectures have to be explored in the light of emerging new device technologies. Using memristive device technology [3)] to enable new computing paradigms such as computation-in-memory architecture [4)] – [7)] is one of the emerging alternatives that could provide a huge potential in terms of energy and computing efficiency.

KW - Special issues and sections

KW - Memristors

KW - Logic circuits

KW - Design automation

KW - Hardware

KW - Nonvolatile memory

KW - Integrated circuit interconnections

KW - Computer architecture

UR - http://www.scopus.com/inward/record.url?scp=85058081260&partnerID=8YFLogxK

U2 - 10.1109/TVLSI.2018.2878679

DO - 10.1109/TVLSI.2018.2878679

M3 - Editorial

VL - 26

SP - 2581

EP - 2583

JO - IEEE Transactions on Very Large Scale Integration (VLSI) Systems

T2 - IEEE Transactions on Very Large Scale Integration (VLSI) Systems

JF - IEEE Transactions on Very Large Scale Integration (VLSI) Systems

SN - 1063-8210

IS - 12

M1 - 8554356

ER -

ID: 51910216