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 proceedingConference contribution

1 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 languageEnglish (US)
Title of host publicationESSCIRC 2023 - IEEE 49th European Solid State Circuits Conference
PublisherIEEE Computer Society
Pages117-120
Number of pages4
ISBN (Electronic)9798350304206
DOIs
StatePublished - 2023
Externally publishedYes
Event49th IEEE European Solid State Circuits Conference, ESSCIRC 2023 - Lisbon, Portugal
Duration: Sep 11 2023Sep 14 2023

Publication series

NameEuropean Solid-State Circuits Conference
Volume2023-September
ISSN (Print)1930-8833

Conference

Conference49th IEEE European Solid State Circuits Conference, ESSCIRC 2023
Country/TerritoryPortugal
CityLisbon
Period9/11/239/14/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Compute-in-Memory
  • Genome Sequencing Alignment
  • RRAM

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