Acute graft-versus-host (GVHD) is a deadly disease that can be treated through fecal microbiota transplantation. However, such treatment is often followed by life-threatening bloodstream infections (BSI). Rapid detection of BSI-causing bacteria is critical in preventing BSI-related deaths. PC-CAM is a pathogen identification system-on-chip designed to assist in avoiding BSI by real-time detection of pathogen bacterial genomes using k-mer matching. The core of PC-CAM is an Approximate search-capable (Hamming distance tolerant) Content Addressable Memory (ACAM). PC-CAM was designed and manufactured in a commercial 65nm process. We use PC-CAM for real-time detection of bacteria in blood and stool samples of GVHD patients and evaluate PC-CAM bacteria identification efficiency, performance, silicon area, and power consumption based on silicon measurements. PC-CAM is capable of classifying 960K short DNA reads/sec within a silicon area of 2.38mm consuming about 1.27mW. We envision PC-CAM as a platform deployed at points of care to provide real-time, accurate, privacy-preserving, easy-to-operate, and energy-efficient pathogen classification.

Integrated BSI bacteria identifier-on-chip using approximate k-mer matching

Garzon, Esteban;Teman, Adam;
2026-01-01

Abstract

Acute graft-versus-host (GVHD) is a deadly disease that can be treated through fecal microbiota transplantation. However, such treatment is often followed by life-threatening bloodstream infections (BSI). Rapid detection of BSI-causing bacteria is critical in preventing BSI-related deaths. PC-CAM is a pathogen identification system-on-chip designed to assist in avoiding BSI by real-time detection of pathogen bacterial genomes using k-mer matching. The core of PC-CAM is an Approximate search-capable (Hamming distance tolerant) Content Addressable Memory (ACAM). PC-CAM was designed and manufactured in a commercial 65nm process. We use PC-CAM for real-time detection of bacteria in blood and stool samples of GVHD patients and evaluate PC-CAM bacteria identification efficiency, performance, silicon area, and power consumption based on silicon measurements. PC-CAM is capable of classifying 960K short DNA reads/sec within a silicon area of 2.38mm consuming about 1.27mW. We envision PC-CAM as a platform deployed at points of care to provide real-time, accurate, privacy-preserving, easy-to-operate, and energy-efficient pathogen classification.
2026
Approximate search
CAM
Content addressable memory
DNA detection and classification
Hamming Distance
RISC-V application processor
SoC
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/400859
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