Computational Associative Memory Powered by Ferroelectric Memory

Kai Ni; Yi Xiao; Shan Deng; Vijaykrishnan Narayanan

doi:10.1109/DRC58590.2023.10187048

Computational Associative Memory Powered by Ferroelectric Memory

Kai Ni
, Yi Xiao
, Shan Deng
, Vijaykrishnan Narayanan

Computer Science and Engineering

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

2 Scopus citations

Abstract

Discovery of ferroelectricity in thin doped Hf02 has revived the interest in ferroelectric memories because its excellent CMOS-compatibility, great scalability, and superior energy efficiency. Various types of ferroelectric memories are under consideration, including capacitor based ferroelectric random access memory (FeRAM) and transistor based ferroelectric field effect transistor (FeFET). Significant progress has been made by integrating them at advanced technology nodes. These exciting developments have made ferroelectric memories prime candidates as embedded nonvolatile memory and enable their application in compute-in-memory (CiM) accelerators. One important class of CiM kernel is content addressable memory (CAM), where the memory is addressed through its content. In this work, we overview different designs of FeFET based CAM and propose FeRAM based CAM.

Original language	English (US)
Title of host publication	2023 Device Research Conference, DRC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350323108
DOIs	https://doi.org/10.1109/DRC58590.2023.10187048
State	Published - 2023
Event	2023 Device Research Conference, DRC 2023 - Santa Barbara, United States Duration: Jun 25 2023 → Jun 28 2023

Publication series

Name	Device Research Conference - Conference Digest, DRC
Volume	2023-June
ISSN (Print)	1548-3770

Conference

Conference	2023 Device Research Conference, DRC 2023
Country/Territory	United States
City	Santa Barbara
Period	6/25/23 → 6/28/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.1109/DRC58590.2023.10187048

Cite this

@inproceedings{b0b337e4c711414ba4c278e3ad4cb5df,

title = "Computational Associative Memory Powered by Ferroelectric Memory",

abstract = "Discovery of ferroelectricity in thin doped Hf02 has revived the interest in ferroelectric memories because its excellent CMOS-compatibility, great scalability, and superior energy efficiency. Various types of ferroelectric memories are under consideration, including capacitor based ferroelectric random access memory (FeRAM) and transistor based ferroelectric field effect transistor (FeFET). Significant progress has been made by integrating them at advanced technology nodes. These exciting developments have made ferroelectric memories prime candidates as embedded nonvolatile memory and enable their application in compute-in-memory (CiM) accelerators. One important class of CiM kernel is content addressable memory (CAM), where the memory is addressed through its content. In this work, we overview different designs of FeFET based CAM and propose FeRAM based CAM.",

author = "Kai Ni and Yi Xiao and Shan Deng and Vijaykrishnan Narayanan",

note = "Funding Information: Acknowledgement: This work is partially supported by SUPREME and PRISM centers, two of SRC/JUMP 2.0 centers. It is also partially supported by NSF under award \#2239284. Reference: [1] K. Ni et al., Nature Electronics, vol. 2, p. 521, (2019). [2] X. Ma et al., IEEE Trans. VLSI Systems, vol. 30, p. 1770, (2022). [3] X. Yin et al., arXiv 2209.11971 (2022). [4] S. M. Yoon et al., IEEE Trans. Electron Devices, vol.48, p.2002, (2001). [5] S. Deng et al., VLSI Symp. (2020). Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Device Research Conference, DRC 2023 ; Conference date: 25-06-2023 Through 28-06-2023",

year = "2023",

doi = "10.1109/DRC58590.2023.10187048",

language = "English (US)",

series = "Device Research Conference - Conference Digest, DRC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 Device Research Conference, DRC 2023",

address = "United States",

}

Ni, K, Xiao, Y, Deng, S & Narayanan, V 2023, Computational Associative Memory Powered by Ferroelectric Memory. in 2023 Device Research Conference, DRC 2023. Device Research Conference - Conference Digest, DRC, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 2023 Device Research Conference, DRC 2023, Santa Barbara, United States, 6/25/23. https://doi.org/10.1109/DRC58590.2023.10187048

TY - GEN

T1 - Computational Associative Memory Powered by Ferroelectric Memory

AU - Ni, Kai

AU - Xiao, Yi

AU - Deng, Shan

AU - Narayanan, Vijaykrishnan

N1 - Funding Information: Acknowledgement: This work is partially supported by SUPREME and PRISM centers, two of SRC/JUMP 2.0 centers. It is also partially supported by NSF under award #2239284. Reference: [1] K. Ni et al., Nature Electronics, vol. 2, p. 521, (2019). [2] X. Ma et al., IEEE Trans. VLSI Systems, vol. 30, p. 1770, (2022). [3] X. Yin et al., arXiv 2209.11971 (2022). [4] S. M. Yoon et al., IEEE Trans. Electron Devices, vol.48, p.2002, (2001). [5] S. Deng et al., VLSI Symp. (2020). Publisher Copyright: © 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - Discovery of ferroelectricity in thin doped Hf02 has revived the interest in ferroelectric memories because its excellent CMOS-compatibility, great scalability, and superior energy efficiency. Various types of ferroelectric memories are under consideration, including capacitor based ferroelectric random access memory (FeRAM) and transistor based ferroelectric field effect transistor (FeFET). Significant progress has been made by integrating them at advanced technology nodes. These exciting developments have made ferroelectric memories prime candidates as embedded nonvolatile memory and enable their application in compute-in-memory (CiM) accelerators. One important class of CiM kernel is content addressable memory (CAM), where the memory is addressed through its content. In this work, we overview different designs of FeFET based CAM and propose FeRAM based CAM.

AB - Discovery of ferroelectricity in thin doped Hf02 has revived the interest in ferroelectric memories because its excellent CMOS-compatibility, great scalability, and superior energy efficiency. Various types of ferroelectric memories are under consideration, including capacitor based ferroelectric random access memory (FeRAM) and transistor based ferroelectric field effect transistor (FeFET). Significant progress has been made by integrating them at advanced technology nodes. These exciting developments have made ferroelectric memories prime candidates as embedded nonvolatile memory and enable their application in compute-in-memory (CiM) accelerators. One important class of CiM kernel is content addressable memory (CAM), where the memory is addressed through its content. In this work, we overview different designs of FeFET based CAM and propose FeRAM based CAM.

UR - https://www.scopus.com/pages/publications/85167870033

UR - https://www.scopus.com/pages/publications/85167870033#tab=citedBy

U2 - 10.1109/DRC58590.2023.10187048

DO - 10.1109/DRC58590.2023.10187048

M3 - Conference contribution

AN - SCOPUS:85167870033

T3 - Device Research Conference - Conference Digest, DRC

BT - 2023 Device Research Conference, DRC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 Device Research Conference, DRC 2023

Y2 - 25 June 2023 through 28 June 2023

ER -

Computational Associative Memory Powered by Ferroelectric Memory

Abstract

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UN SDGs

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