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Toward an Embedded SoC for Mobile DNA Sequencing Applications.

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    This summary is machine-generated.

    This study introduces a novel System-on-Chip (SoC) for portable DNA sequencing, integrating bioinformatics accelerators for faster basecalling and sequence alignment. This innovation paves the way for fully embedded, mobile genomic analysis.

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    Area of Science:

    • Genomics and Bioinformatics
    • Computer Engineering
    • Hardware Acceleration

    Background:

    • Portable DNA sequencing technologies are advancing rapidly.
    • Current portable sequencers require bulky external systems for bioinformatics analysis.
    • There is a need for integrated, efficient computing solutions for mobile genomics.

    Purpose of the Study:

    • To propose and evaluate an embedded System-on-Chip (SoC) architecture for mobile DNA sequencing.
    • To develop specialized hardware accelerators for key bioinformatics tasks.
    • To demonstrate the feasibility of on-device bioinformatics processing.

    Main Methods:

    • Designed a heterogeneous SoC leveraging the RISC-V architecture.
    • Incorporated two dedicated accelerators: one for deep learning-based basecalling and another for sequence alignment.
    • Implemented the basecalling accelerator on an FPGA and evaluated its performance and energy efficiency.

    Main Results:

    • The basecalling accelerator achieved a ~2000x speedup and superior energy efficiency compared to CPUs and GPUs.
    • The sequence alignment accelerator demonstrated significant performance-per-joule advantages over existing platforms.
    • A preliminary ASIC prototype operated at 250 MHz with low power consumption (34 mW).

    Conclusions:

    • The proposed SoC architecture offers a viable solution for integrated bioinformatics in mobile DNA sequencing.
    • Hardware acceleration is crucial for achieving high performance and energy efficiency in portable genomic applications.
    • This work represents a significant step towards fully embedded bioinformatics hardware for ubiquitous genomic analysis.