ClaPIM: Scalable Sequence CLAssification using Processing-In-Memory
This addresses the need for fast, high-quality DNA sequence classification in computational biology, with applications in disease prevention and drug design, representing a novel advancement in hardware acceleration.
The paper tackles DNA sequence classification by introducing ClaPIM, a scalable architecture using memristive processing-in-memory, which achieves up to 20x higher F1 score and 1.8x throughput improvement compared to Kraken2, and 30.4x normalized throughput per area improvement over EDAM.
DNA sequence classification is a fundamental task in computational biology with vast implications for applications such as disease prevention and drug design. Therefore, fast high-quality sequence classifiers are significantly important. This paper introduces ClaPIM, a scalable DNA sequence classification architecture based on the emerging concept of hybrid in-crossbar and near-crossbar memristive processing-in-memory (PIM). We enable efficient and high-quality classification by uniting the filter and search stages within a single algorithm. Specifically, we propose a custom filtering technique that drastically narrows the search space and a search approach that facilitates approximate string matching through a distance function. ClaPIM is the first PIM architecture for scalable approximate string matching that benefits from the high density of memristive crossbar arrays and the massive computational parallelism of PIM. Compared with Kraken2, a state-of-the-art software classifier, ClaPIM provides significantly higher classification quality (up to 20x improvement in F1 score) and also demonstrates a 1.8x throughput improvement. Compared with EDAM, a recently-proposed SRAM-based accelerator that is restricted to small datasets, we observe both a 30.4x improvement in normalized throughput per area and a 7% increase in classification precision.