A 65nm RRAM Compute-in-Memory Macro for Genome Sequencing Alignment

Fan Zhang; Wangxin He; Injune Yeo; Maximilian Liehr; Nathaniel Cady; Yu Cao; Jae Sun Seo; Deliang Fan

doi:10.1109/ESSCIRC59616.2023.10268783

A 65nm RRAM Compute-in-Memory Macro for Genome Sequencing Alignment

Fan Zhang
, Wangxin He
, Injune Yeo
, Maximilian Liehr
, Nathaniel Cady
, Yu Cao
, Jae Sun Seo
, Deliang Fan

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

3 Scopus citations

Abstract

In genomic analysis, the major computation bottleneck is the memory-and compute-intensive DNA short reads alignment due to memory-wall challenge. This work presents the first Resistive RAM (RRAM) based Compute-in-Memory (CIM) macro design for accelerating state-of-the-art BWT based genome sequencing alignment. Our design could support all the core instructions, i.e., XNOR based match, count, and addition, required by alignment algorithm. The proposed CIM macro implemented in integration of HfO2 RRAM and 65nm CMOS demonstrates the best energy efficiency to date with 2.07 TOPS/W and 2. 12Gsuffixes/J at 1. 0V.

Original language	English (US)
Title of host publication	ESSCIRC 2023 - IEEE 49th European Solid State Circuits Conference
Publisher	IEEE Computer Society
Pages	117-120
Number of pages	4
ISBN (Electronic)	9798350304206
DOIs	https://doi.org/10.1109/ESSCIRC59616.2023.10268783
State	Published - 2023
Externally published	Yes
Event	49th IEEE European Solid State Circuits Conference, ESSCIRC 2023 - Lisbon, Portugal Duration: Sep 11 2023 → Sep 14 2023

Publication series

Name	European Solid-State Circuits Conference
Volume	2023-September
ISSN (Print)	1930-8833

Conference

Conference	49th IEEE European Solid State Circuits Conference, ESSCIRC 2023
Country/Territory	Portugal
City	Lisbon
Period	9/11/23 → 9/14/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Compute-in-Memory
Genome Sequencing Alignment
RRAM

Publisher link

10.1109/ESSCIRC59616.2023.10268783

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Zhang, F., He, W., Yeo, I., Liehr, M., Cady, N., Cao, Y., Seo, J. S., & Fan, D. (2023). A 65nm RRAM Compute-in-Memory Macro for Genome Sequencing Alignment. In ESSCIRC 2023 - IEEE 49th European Solid State Circuits Conference (pp. 117-120). (European Solid-State Circuits Conference; Vol. 2023-September). IEEE Computer Society. https://doi.org/10.1109/ESSCIRC59616.2023.10268783

A 65nm RRAM Compute-in-Memory Macro for Genome Sequencing Alignment. / Zhang, Fan; He, Wangxin; Yeo, Injune et al.
ESSCIRC 2023 - IEEE 49th European Solid State Circuits Conference. IEEE Computer Society, 2023. p. 117-120 (European Solid-State Circuits Conference; Vol. 2023-September).

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

Zhang, F, He, W, Yeo, I, Liehr, M, Cady, N, Cao, Y, Seo, JS & Fan, D 2023, A 65nm RRAM Compute-in-Memory Macro for Genome Sequencing Alignment. in ESSCIRC 2023 - IEEE 49th European Solid State Circuits Conference. European Solid-State Circuits Conference, vol. 2023-September, IEEE Computer Society, pp. 117-120, 49th IEEE European Solid State Circuits Conference, ESSCIRC 2023, Lisbon, Portugal, 9/11/23. https://doi.org/10.1109/ESSCIRC59616.2023.10268783

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abstract = "In genomic analysis, the major computation bottleneck is the memory-and compute-intensive DNA short reads alignment due to memory-wall challenge. This work presents the first Resistive RAM (RRAM) based Compute-in-Memory (CIM) macro design for accelerating state-of-the-art BWT based genome sequencing alignment. Our design could support all the core instructions, i.e., XNOR based match, count, and addition, required by alignment algorithm. The proposed CIM macro implemented in integration of HfO2 RRAM and 65nm CMOS demonstrates the best energy efficiency to date with 2.07 TOPS/W and 2. 12Gsuffixes/J at 1. 0V.",

keywords = "Compute-in-Memory, Genome Sequencing Alignment, RRAM",

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AU - Cao, Yu

AU - Seo, Jae Sun

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AB - In genomic analysis, the major computation bottleneck is the memory-and compute-intensive DNA short reads alignment due to memory-wall challenge. This work presents the first Resistive RAM (RRAM) based Compute-in-Memory (CIM) macro design for accelerating state-of-the-art BWT based genome sequencing alignment. Our design could support all the core instructions, i.e., XNOR based match, count, and addition, required by alignment algorithm. The proposed CIM macro implemented in integration of HfO2 RRAM and 65nm CMOS demonstrates the best energy efficiency to date with 2.07 TOPS/W and 2. 12Gsuffixes/J at 1. 0V.

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A 65nm RRAM Compute-in-Memory Macro for Genome Sequencing Alignment

Abstract

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Bibliographical note

Keywords

Publisher link

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