Sequencing on Silicon: AI SoC Design for Mobile Genomics at the Edge

Abstract

This paper presents a specialized CMOS System-on-Chip (SoC) designed to handle the extremely high data rates of mobile nanopore DNA sequencing, advancing genomic analysis at the edge. The proposed architecture employs a heterogeneous compute fabric centered around a multi-core RISC-V processor, tightly integrated with deep learning and bioinformatics accelerators. Utilizing hardware/software co-design, the SoC achieves the necessary energy efficiency for real-time, on-device genome interpretation.

Report

Key Highlights

• Domain Focus: The SoC is specifically designed for mobile genetic analysis, leveraging deep learning for signal processing and interpretation of raw nanopore sequencing data.
• Data Rate Challenge: Addresses the critical challenge posed by nanopore sequencing, which produces raw data rates over 100X higher than typical time-series data like audio, necessitating aggressive compute strategies.
• Core Innovation: The primary innovation is a specialized CMOS SoC built for energy-efficient operation in mobile contexts.
• Design Strategy: Employs a comprehensive hardware/software co-design approach to ensure energy-efficient operation across the heterogeneous compute fabric.
• Goal: Achieves real-time, on-device genome analysis, advancing next-generation mobile genomics.

Technical Details

• Device Type: CMOS System-on-Chip (SoC).
• Main Processor: Multi-core RISC-V processor serves as the general-purpose compute element.
• Accelerators: Includes tightly coupled, domain-specific hardware accelerators for both deep learning (for low-level signal processing/basecalling) and bioinformatics (for high-level genomic interpretation).
• Architecture: Heterogeneous compute fabric optimized for aggressive compute and memory handling required by sequencing data.
• Optimization Target: Energy-efficient, real-time processing necessary for edge deployment.

Implications

• RISC-V in Emerging Tech: This work solidifies RISC-V as a viable, customizable architecture for high-stakes, compute-intensive applications outside of traditional embedded systems, particularly in the emerging field of mobile genomics and bioinformatics.
• Custom Accelerator Validation: The design strategy validates the core advantage of the RISC-V ecosystem: the ability to seamlessly integrate custom, tightly coupled accelerators for domain-specific tasks (Deep Learning and Genomics), maximizing performance per watt.
• Edge AI Leadership: The SoC pushes the boundaries of edge computing, demonstrating that complex tasks previously relegated to cloud infrastructure (full genome interpretation) can be performed efficiently on-device, enabling immediate, decentralized analysis critical for personalized medicine and field diagnostics.
• Hardware/Software Co-Design Paradigm: The success relies on the tight coupling enabled by RISC-V's flexibility, setting a precedent for using hardware/software co-design to solve complex data bottlenecks in future high-throughput sensors.