Real-time raw signal genomic analysis using fully integrated memristor hardware

2025-04-22

AI Summary

Conventional von Neumann–based real-time genomic analysis suffers from prohibitive data movement overhead, hindering field-deployable pathogen detection using portable sequencers. This work proposes the first fully integrated memristor chip enabling end-to-end analog genomic analysis: it bypasses digital basecalling and alignment entirely, instead performing joint signal decoding and sequence alignment directly within memristor-based in-memory computing. We innovatively exploit intrinsic device noise to implement analog-domain locality-sensitive hashing (LSH), synergistically combined with content-addressable memory (CAM) for single-step approximate search. Experimental results demonstrate 97.15% F1-score for viral raw analog signal alignment—outperforming state-of-the-art ASICs by 51× in speed and 477× in energy efficiency. To our knowledge, this is the first demonstration of real-time, ultra-low-power, portable metagenomic classification and pathogen identification.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Metareasoning and Metaheuristics
• Cognitive Modeling & Cognitive Systems: Analogy

Application Category

• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, hampering on-site genomic analysis due to prohibitive time and energy costs. These technologies generate a massive amount of noisy analog signals that traditionally require basecalling and digital mapping, both demanding frequent and costly data movement on von Neumann hardware. To overcome these challenges, we present a memristor-based hardware-software co-design that processes raw sequencer signals directly in analog memory, effectively combining the separated basecalling and read mapping steps. Here we demonstrate, for the first time, end-to-end memristor-based genomic analysis in a fully integrated memristor chip. By exploiting intrinsic device noise for locality-sensitive hashing and implementing parallel approximate searches in content-addressable memory, we experimentally showcase on-site applications including infectious disease detection and metagenomic classification. Our experimentally-validated analysis confirms the effectiveness of this approach on real-world tasks, achieving a state-of-the-art 97.15% F1 score in virus raw signal mapping, with 51x speed up and 477x energy saving compared to implementation on a state-of-the-art ASIC. These results demonstrate that memristor-based in-memory computing provides a viable solution for integration with portable sequencers, enabling truly real-time on-site genomic analysis for applications ranging from pathogen surveillance to microbial community profiling.

Problem

• Overcoming real-time genomic data processing bottlenecks
• Reducing time and energy costs in analog signal analysis
• Enabling portable on-site genomic sequencing applications

Innovation

• Memristor-based hardware-software co-design
• Analog in-memory signal processing
• Parallel approximate searches in memory