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.

翻译：微型DNA测序硬件已开始在移动场景中取得成功，推动了对边缘高效机器学习的需求。该领域利用语音和时间序列分析中常见的深度学习技术，同时用于底层信号处理和高层基因组解读。然而与音频不同，纳米孔测序产生的原始数据速率高出100倍以上，需要更激进的算力和内存处理方案。本文提出一种专为移动基因分析设计的CMOS片上系统（SoC）。我们的方法将多核RISC-V处理器与深度学习及生物信息学的紧耦合加速器相结合。通过硬件/软件协同设计策略，在异构计算架构上实现能效优化的运行，以设备端实时基因组分析为目标。这项工作展示了深度学习、边缘计算与领域专用硬件的融合，以推动下一代移动基因组学的发展。