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Genome sequence analysis is a powerful tool in medical and scientificresearch. Considering the inevitable sequencing errors and genetic variations,approximate string matching (ASM) has been adopted in practice for genomesequencing. However, with exponentially increasing bio-data, ASM hardwareacceleration is facing severe challenges in improving the throughput and energyefficiency with the accuracy constraint. This paper presents ASMCap, an ASMacceleration approach for genome sequence analysis with hardware-algorithmco-optimization. At the circuit level, ASMCap adopts charge-domain computingbased on the capacitive multi-level content addressable memories (ML-CAMs), andoutperforms the state-of-the-art ML-CAM-based ASM accelerators EDAM with higheraccuracy and energy efficiency. ASMCap also has misjudgment correctioncapability with two proposed hardware-friendly strategies, namely theHamming-Distance Aid Correction (HDAC) for the substitution-dominant edits andthe Threshold-Aware Sequence Rotation (TASR) for the consecutive indels.Evaluation results show that ASMCap can achieve an average of 1.2x (from 74.7%to 87.6%) and up to 1.8x (from 46.3% to 81.2%) higher F1 score (the key metricof accuracy), 1.4x speedup, and 10.8x energy efficiency improvement comparedwith EDAM. Compared with the other ASM accelerators, including ResMA based onthe comparison matrix, and SaVI based on the seeding strategy, ASMCap achievesan average improvement of 174x and 61x speedup, and 8.7e3x and 943x higherenergy efficiency, respectively.

ASMCap: An Approximate String Matching Accelerator for Genome Sequence Analysis Based on Capacitive Content Addressable Memory