Hamming Distance Tolerant Content-Addressable Memory (HD-CAM) for DNA Classification

Esteban Garzón, Roman Golman, Zuher Jahshan, Robert Hanhan, Natan Vinshtok-Melnik, Marco Lanuzza, Adam Teman, Leonid Yavits

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper proposes a novel Hamming distance tolerant content-addressable memory (HD-CAM) for energy-efficient in-memory approximate matching applications. HD-CAM exploits NOR-type based static associative memory bitcells, where we add circuitry to enable approximate search with programmable tolerance. HD-CAM implements approximate search using matchline charge redistribution rather than its rise or fall time, frequently employed in state-of-the-art solutions. HD-CAM was designed in a 65 nm 1.2 V CMOS technology and evaluated through extensive Monte Carlo simulations. Our analysis shows that HD-CAM supports robust operation under significant process variations and changes in the design parameters, enabling a wide range of mismatch threshold (tolerable Hamming distance) levels and pattern lengths. HD-CAM was functionally evaluated for virus DNA classification, which makes HD-CAM suitable for hardware acceleration of genomic surveillance of viral outbreaks, such as Covid-19 pandemics.

Original languageEnglish
Pages (from-to)28080-28093
Number of pages14
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Bibliographical note

Funding Information:
This work was supported by the Israel Science Foundation under Grant 996/18.

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Approximate search
  • DNA classification
  • content addressable memory
  • hamming distance (HD)

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