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

arXiv (Cornell University)

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.

Authors 9

1. Peiyi He

   University of Hong Kong

   Affiliation as printed

   Department of Electrical and Electronic Engineering , The University of Hong Kong , Hong Kong SAR , China

2. Shengbo Wang

   University of Hong Kong

   Affiliation as printed

   Department of Electrical and Electronic Engineering , The University of Hong Kong , Hong Kong SAR , China

3. Ruibin Mao

   University of Hong Kong

   Affiliation as printed

   Department of Electrical and Electronic Engineering , The University of Hong Kong , Hong Kong SAR , China

4. Sebastian Siegel

   Forschungszentrum Jülich

   Affiliation as printed

   Peter Gr ünberg Institute (PGI-14) , Forschungszentrum J ülich GmbH , J ülich , Germany

5. Giacomo Pedretti

   Hewlett Packard Enterprise (United States)

   Affiliation as printed

   Hewlett Packard Labs , Hewlett Packard Enterprise , Milpitas , CA , USA

6. Jim Ignowski

   Hewlett Packard Enterprise (United States)

   Affiliation as printed

   Hewlett Packard Labs , Hewlett Packard Enterprise , Milpitas , CA , USA

7. John Paul Strachan Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Peter Gr ünberg Institute (PGI-14) , Forschungszentrum J ülich GmbH , J ülich , Germany

   RWTH Aachen University , Aachen , Germany

8. Ruibang Luo

   University of Hong Kong

   Affiliation as printed

   Department of Computer Science , School of Computing and Data Science , The University of Hong Kong , Hong Kong SAR , China

9. Can Li

   University of Hong Kong

   Affiliation as printed

   Department of Electrical and Electronic Engineering , The University of Hong Kong , Hong Kong SAR , China