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 searchsolutions 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× higher F1 score for high-quality DNA reads and up to 6.2×higher 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× average speedup over Kraken2.