GeNVoM: Read Mapping Near Non-Volatile Memory

DNA sequencing is the physical/biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand. As semiconductor technology revolutionized computing, modern DNA sequencing technology (termed Next Generation Sequencing, NGS) revolutionized genomic research. As a result, modern NGSplatforms can sequence hundreds ofmillions of short DNA fragments in parallel. The sequenced DNA fragments, representing the output of NGS platforms, are termed reads. Besides genomic variations, NGSimperfections induce noise in reads. Mapping each read to (the most similar portion of) a reference genome of the same species, i.e., read mapping, is a common critical first step in a diverse set of emerging bioinformatics applications. Mapping represents a search-heavymemory-intensive similarity matching problem, therefore, can greatly benefit fromnear-memory processing. Intuition suggests using fast associative search enabled by Ternary Content AddressableMemory (TCAM) by construction. However, the excessive energy consumption and lack of support for similarity matching (underNGS and genomic variation induced noise) renders direct application of TCAMinfeasible, irrespective of volatility, where only non-volatile TCAMcan accommodate the largememory footprint in an area-efficient way. This paper introduces GeNVoM, a scalable, energy-efficient and high-throughput solution. Instead of optimizing an algorithm developed for general-purpose computers or GPUs, GeNVoM rethinks the algorithm and non-volatile TCAM-based accelerator design together from the ground up. Thereby GeNVoM can improve the throughput by up to 3.67x; the energy consumption, by up to 1.36x, when compared to an ASIC baseline, which represents one of the highest-throughput implementations known.