research-article

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KrakenOnMem: a memristor-augmented HW/SW framework for taxonomic profiling

Authors:

Taha Shahroodi,

Abhairaj Singh,

Said HamdiouiAuthors Info & Claims

ICS '22: Proceedings of the 36th ACM International Conference on Supercomputing

Article No.: 29, Pages 1 - 14

https://doi.org/10.1145/3524059.3532367

Published: 28 June 2022 Publication History

Abstract

State-of-the-art taxonomic profilers that comprise the first step in larger-context metagenomic studies have proven to be computationally intensive, i.e., while accurate, they come at the cost of high latency and energy consumption. Table Lookup operation is a primary bottleneck of today's profilers. In this paper, we first propose TL-PIM, a hardware accelerator based on the processing-in-memory (PIM) paradigm to accelerate Table Lookup. TL-PIM leverages the in-memory compute capability of emerging memory technologies along with intelligent data mapping. Then, we integrate TL-PIM into Kraken2, a state-of-the-art metagenomic profiler, and build an HW/SW co-designed profiler, called KrakenOnMem. Results from a silicon-based prototype of our emerging memory validate the design and required operations on a smaller scale. Our large-scale calibrated simulations show that KrakenOnMem can provide an average of 61.3% speedup compared to original Kraken2 for end-to-end profiling. Additionally, our design improves the energy consumption by orders of magnitude compared to the original Kraken2 while incurring a negligible area overhead.

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Index Terms

KrakenOnMem: a memristor-augmented HW/SW framework for taxonomic profiling
1. Hardware
  1. Emerging technologies
    1. Biology-related information processing
      1. Bio-embedded electronics
    2. Memory and dense storage

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cover image ACM Conferences

ICS '22: Proceedings of the 36th ACM International Conference on Supercomputing

June 2022

514 pages

ISBN:9781450392815

DOI:10.1145/3524059

General Chairs:
Lawrence Rauchwerger
University of Illinois at Urbana-Champaign
,
Kirk Cameron
Virginia Tech
,
Program Chairs:
Dimitrios S. Nikolopoulos
Virginia Tech
,
Dionisios Pnevmatikatos
National Technical University of Athens

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Shahroodi TMiao MZahedi MWong SHamdioui S(2023)SieveMem: A Computation-in-Memory Architecture for Fast and Accurate Pre-Alignment2023 IEEE 34th International Conference on Application-specific Systems, Architectures and Processors (ASAP)10.1109/ASAP57973.2023.00035(156-164)Online publication date: Jul-2023
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Zahedi MShahroodi TWong SHamdioui S(2023)Efficient Signed Arithmetic Multiplication on Memristor-Based CrossbarIEEE Access10.1109/ACCESS.2023.326325911(33964-33978)Online publication date: 2023
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