Sequencing on Silicon: AI SoC Design for Mobile Genomics at the Edge
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866914086881591296 |
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| author | Magierowski, Sebastian Wu, Zhongpan Beyene, Abel Hammad, Karim |
| author_facet | Magierowski, Sebastian Wu, Zhongpan Beyene, Abel Hammad, Karim |
| contents | Miniature DNA sequencing hardware has begun to succeed in mobile contexts, driving demand for efficient machine learning at the edge. This domain leverages deep learning techniques familiar from speech and time-series analysis for both low-level signal processing and high-level genomic interpretation. Unlike audio, however, nanopore sequencing presents raw data rates over 100X higher, requiring more aggressive compute and memory handling. In this paper, we present a CMOS system-on-chip (SoC) designed for mobile genetic analysis. Our approach combines a multi-core RISC-V processor with tightly coupled accelerators for deep learning and bioinformatics. A hardware/software co-design strategy enables energy-efficient operation across a heterogeneous compute fabric, targeting real-time, on-device genome analysis. This work exemplifies the integration of deep learning, edge computing, and domain-specific hardware to advance next-generation mobile genomics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sequencing on Silicon: AI SoC Design for Mobile Genomics at the Edge Magierowski, Sebastian Wu, Zhongpan Beyene, Abel Hammad, Karim Hardware Architecture Emerging Technologies Miniature DNA sequencing hardware has begun to succeed in mobile contexts, driving demand for efficient machine learning at the edge. This domain leverages deep learning techniques familiar from speech and time-series analysis for both low-level signal processing and high-level genomic interpretation. Unlike audio, however, nanopore sequencing presents raw data rates over 100X higher, requiring more aggressive compute and memory handling. In this paper, we present a CMOS system-on-chip (SoC) designed for mobile genetic analysis. Our approach combines a multi-core RISC-V processor with tightly coupled accelerators for deep learning and bioinformatics. A hardware/software co-design strategy enables energy-efficient operation across a heterogeneous compute fabric, targeting real-time, on-device genome analysis. This work exemplifies the integration of deep learning, edge computing, and domain-specific hardware to advance next-generation mobile genomics. |
| title | Sequencing on Silicon: AI SoC Design for Mobile Genomics at the Edge |
| topic | Hardware Architecture Emerging Technologies |
| url | https://arxiv.org/abs/2510.09339 |