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    We developed a novel hardware architecture for approximate string matching (ASM) using Myers bit-vector algorithm. This design accelerates biological data searches, offering significant performance and energy efficiency gains.

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    Area of Science:

    • Computer Science
    • Bioinformatics
    • Computational Biology

    Background:

    • Approximate string matching (ASM) is crucial for analyzing large biological datasets in genomics and proteomics.
    • Existing ASM algorithms, while fast, are computationally intensive, requiring acceleration for massive data volumes.
    • Myers bit-vector algorithm offers efficient edit distance calculation using bit-parallel operations.

    Purpose of the Study:

    • To propose a novel hardware architecture for accelerating approximate string matching (ASM).
    • To enable parallel searching of multiple query patterns within biological databases.
    • To achieve energy-efficient hardware implementation of the ASM algorithm.

    Main Methods:

    • Leveraging Myers bit-vector algorithm for approximate string matching.
    • Designing a parallel architecture with multiple processing engines.
    • Employing hardware/software codesign for optimized performance and energy efficiency.

    Main Results:

    • The proposed architecture accelerates ASM by achieving 22x better performance compared to related work.
    • Demonstrates exceptional energy efficiency, reaching approximately 500x10^9 cell updates per joule.
    • Enables efficient parallel searching of multiple query patterns in biological databases.

    Conclusions:

    • The novel parallel ASM architecture significantly enhances the speed and energy efficiency of biological data analysis.
    • This hardware-accelerated approach is vital for handling the computational demands of modern genomics and proteomics.
    • The design offers a practical solution for faster and more sustainable processing of large-scale biological sequence data.