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.

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