We propose GenMClass, a genome classification system-on-chip (SoC) implementing two different classification approaches and comprising two separate classification engines: a DNN accelerator GenDNN, that classifies DNA reads converted to images using a classification neural network, and a similarity search-capable Error Tolerant Content Addressable Memory ETCAM, that classifies genomes by k-mer matching. Classification operations are controlled by an embedded RISCV processor. GenMClass classification platform was designed and manufactured in a commercial 65 nm process. We conduct a comparative analysis of ETCAM and GenDNN classification efficiency as well as their performance, silicon area and power consumption using silicon measurements. The size of GenMClass SoC is 3.4 mm2 and its total power consumption (assuming both GenDNN and ETCAM perform classification at the same time) is 144 mW. This allows using GenMClass as a portable classifier for pathogen surveillance during pandemics, food safety and environmental monitoring, agriculture pathogen and antimicrobial resistance control, in the field or at points of care.
GenMClass: Design and comparative analysis of genome classifier-on-chip platform
Garzon E.;Teman A.;
2026-01-01
Abstract
We propose GenMClass, a genome classification system-on-chip (SoC) implementing two different classification approaches and comprising two separate classification engines: a DNN accelerator GenDNN, that classifies DNA reads converted to images using a classification neural network, and a similarity search-capable Error Tolerant Content Addressable Memory ETCAM, that classifies genomes by k-mer matching. Classification operations are controlled by an embedded RISCV processor. GenMClass classification platform was designed and manufactured in a commercial 65 nm process. We conduct a comparative analysis of ETCAM and GenDNN classification efficiency as well as their performance, silicon area and power consumption using silicon measurements. The size of GenMClass SoC is 3.4 mm2 and its total power consumption (assuming both GenDNN and ETCAM perform classification at the same time) is 144 mW. This allows using GenMClass as a portable classifier for pathogen surveillance during pandemics, food safety and environmental monitoring, agriculture pathogen and antimicrobial resistance control, in the field or at points of care.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
