DASH-CAM: Dynamic Approximate SearcH Content Addressable Memory for genome classification

Zuher Jahshan, Itay Merlin, Esteban Garzón, Leonid Yavits

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We propose a novel dynamic storage-based approximate search content addressable memory (DASH-CAM) for computational genomics applications, particularly for identification and classification of viral pathogens of epidemic significance. DASH-CAM provides 5.5 × better density compared to state-of-the-art SRAM-based approximate search CAM. This allows using DASH-CAM as a portable classifier that can be applied to pathogen surveillance in low-quality field settings during pandemics, as well as to pathogen diagnostics at points of care. DASH-CAM approximate search capabilities allow a high level of flexibility when dealing with a variety of industrial sequencers with different error profiles. DASH-CAM achieves up to 30% and 20% higher F1 score when classifying DNA reads with 10% error rate, compared to state-of-the-art DNA classification tools MetaCache-GPU and Kraken2 respectively. Simulated at 1GHz, DASH-CAM provides 1, 178 × and 1, 040 × average speedup over MetaCache-GPU and Kraken2 respectively.

Original languageEnglish
Title of host publicationProceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2023
PublisherAssociation for Computing Machinery, Inc
Pages1453-1465
Number of pages13
ISBN (Electronic)9798400703294
DOIs
StatePublished - 28 Oct 2023
Event56th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2023 - Toronto, Canada
Duration: 28 Oct 20231 Nov 2023

Publication series

NameProceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2023

Conference

Conference56th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2023
Country/TerritoryCanada
CityToronto
Period28/10/231/11/23

Bibliographical note

Publisher Copyright:
© 2023 Owner/Author.

Funding

This work was supported by the European Union's Horizon Europe programme for research and innovation under grant agreement No. 101047160. The work of Esteban Garzón was supported by the Italian MUR under the call "Horizon Europe 2021-2027 programme - H25F21001420001" This work was supported by the European Union’s Horizon Europe programme for research and innovation under grant agreement No. 101047160. The work of Esteban Garzón was supported by the Italian MUR under the call “Horizon Europe 2021-2027 programme – H25F21001420001”

FundersFunder number
European Union's Horizon Europe Programme for Research and Innovation
European Union’s Horizon Europe programme for research and innovation101047160
Horizon Europe 2021-2027 programmeH25F21001420001
Ministero dell’Istruzione, dell’Università e della Ricerca

    Keywords

    • Approximate search
    • Content Addressable Memory
    • Dynamic approximate search
    • GC-eDRAM
    • Pathogen classification
    • Pathogen detection

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