DIPER: Detection and Identification of Pathogens Using Edit Distance-Tolerant Resistive CAM

Abstract

We propose a novel resistive edit distance-tolerant content addressable memory for computational genomics applications, particularly for detection and identification of pathogens of pandemic importance. Unlike state-of-the-art approximate search solutions that tolerate small number of replacements between the query pattern and the stored data, DIPER tolerates insertions and deletions, ubiquitous in genomics. DIPER achieves up to 1.7\boldsymbol{\times}× higher \bf{\textit{F}_{1}}F1 score for high-quality DNA reads and up to 6.2\boldsymbol{\times}× higher \bf{\textit{F}_{1}}F1 score for DNA reads with 15% error rate, compared to state-of-the-art DNA classification tool Kraken2. Simulated at 500 MHz, DIPER provides 910\boldsymbol{\times}× average speedup over Kraken2.