Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory

Brian Crafton; Connor Talley; Samuel Spetalnick; Jong Hyeok Yoon; Arijit Raychowdhury

doi:10.1109/ISCAS48785.2022.9937307

Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory

Brian Crafton, Connor Talley, Samuel Spetalnick, Jong Hyeok Yoon, Arijit Raychowdhury

Department of Electrical Engineering and Computer Science

Georgia Institute of Technology

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

1 Scopus citations

Abstract

Compute in-memory (CIM) is an exciting circuit innovation that promises to increase effective memory bandwidth and perform computation on the bitlines of memory sub-arrays. Utilizing embedded non-volatile memories (eNVM) such as resistive random access memory (RRAM), various forms of neural networks can be implemented. Unfortunately, CIM faces new challenges traditional CMOS architectures have avoided. In this work, we characterize the impact of IR-drop and device variation (calibrated with measured data on foundry RRAM) and evaluate different approaches to write verify. Using various voltages and pulse widths we program cells to offset IR-drop and demonstrate a 136.4 times reduction in BER during CIM.

Original language	English
Title of host publication	IEEE International Symposium on Circuits and Systems, ISCAS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	70-74
Number of pages	5
ISBN (Electronic)	9781665484855
DOIs	https://doi.org/10.1109/ISCAS48785.2022.9937307
State	Published - 2022
Event	2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, United States Duration: 27 May 2022 → 1 Jun 2022

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2022-May
ISSN (Print)	0271-4310

Conference

Conference	2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Country/Territory	United States
City	Austin
Period	27/05/22 → 1/06/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Access to Document

10.1109/ISCAS48785.2022.9937307

Cite this

Crafton, B., Talley, C., Spetalnick, S., Yoon, J. H., & Raychowdhury, A. (2022). Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory. In IEEE International Symposium on Circuits and Systems, ISCAS 2022 (pp. 70-74). (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2022-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS48785.2022.9937307

@inproceedings{f56c385fe38f4154833483b4bcdc8491,

title = "Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory",

abstract = "Compute in-memory (CIM) is an exciting circuit innovation that promises to increase effective memory bandwidth and perform computation on the bitlines of memory sub-arrays. Utilizing embedded non-volatile memories (eNVM) such as resistive random access memory (RRAM), various forms of neural networks can be implemented. Unfortunately, CIM faces new challenges traditional CMOS architectures have avoided. In this work, we characterize the impact of IR-drop and device variation (calibrated with measured data on foundry RRAM) and evaluate different approaches to write verify. Using various voltages and pulse widths we program cells to offset IR-drop and demonstrate a 136.4 times reduction in BER during CIM.",

author = "Brian Crafton and Connor Talley and Samuel Spetalnick and Yoon, {Jong Hyeok} and Arijit Raychowdhury",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 ; Conference date: 27-05-2022 Through 01-06-2022",

year = "2022",

doi = "10.1109/ISCAS48785.2022.9937307",

language = "English",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

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

pages = "70--74",

booktitle = "IEEE International Symposium on Circuits and Systems, ISCAS 2022",

}

Crafton, B, Talley, C, Spetalnick, S, Yoon, JH & Raychowdhury, A 2022, Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory. in IEEE International Symposium on Circuits and Systems, ISCAS 2022. Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2022-May, Institute of Electrical and Electronics Engineers Inc., pp. 70-74, 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022, Austin, United States, 27/05/22. https://doi.org/10.1109/ISCAS48785.2022.9937307

Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory. / Crafton, Brian; Talley, Connor; Spetalnick, Samuel et al.
IEEE International Symposium on Circuits and Systems, ISCAS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 70-74 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2022-May).

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

TY - GEN

T1 - Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory

AU - Crafton, Brian

AU - Talley, Connor

AU - Spetalnick, Samuel

AU - Yoon, Jong Hyeok

AU - Raychowdhury, Arijit

PY - 2022

Y1 - 2022

N2 - Compute in-memory (CIM) is an exciting circuit innovation that promises to increase effective memory bandwidth and perform computation on the bitlines of memory sub-arrays. Utilizing embedded non-volatile memories (eNVM) such as resistive random access memory (RRAM), various forms of neural networks can be implemented. Unfortunately, CIM faces new challenges traditional CMOS architectures have avoided. In this work, we characterize the impact of IR-drop and device variation (calibrated with measured data on foundry RRAM) and evaluate different approaches to write verify. Using various voltages and pulse widths we program cells to offset IR-drop and demonstrate a 136.4 times reduction in BER during CIM.

AB - Compute in-memory (CIM) is an exciting circuit innovation that promises to increase effective memory bandwidth and perform computation on the bitlines of memory sub-arrays. Utilizing embedded non-volatile memories (eNVM) such as resistive random access memory (RRAM), various forms of neural networks can be implemented. Unfortunately, CIM faces new challenges traditional CMOS architectures have avoided. In this work, we characterize the impact of IR-drop and device variation (calibrated with measured data on foundry RRAM) and evaluate different approaches to write verify. Using various voltages and pulse widths we program cells to offset IR-drop and demonstrate a 136.4 times reduction in BER during CIM.

UR - http://www.scopus.com/inward/record.url?scp=85142541211&partnerID=8YFLogxK

U2 - 10.1109/ISCAS48785.2022.9937307

DO - 10.1109/ISCAS48785.2022.9937307

M3 - Conference contribution

AN - SCOPUS:85142541211

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

SP - 70

EP - 74

BT - IEEE International Symposium on Circuits and Systems, ISCAS 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022

Y2 - 27 May 2022 through 1 June 2022

ER -

Crafton B, Talley C, Spetalnick S, Yoon JH, Raychowdhury A. Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory. In IEEE International Symposium on Circuits and Systems, ISCAS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 70-74. (Proceedings - IEEE International Symposium on Circuits and Systems). doi: 10.1109/ISCAS48785.2022.9937307

Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this