GCOC:基于相似性搜索内容可寻址内存的片上基因组分类器。

Yuval Harary, Paz Snapir, Shir Siman Tov, Chen Kruphman, Eyal Rechef, Zuher Jahshan, Esteban Garzon, Leonid Yavits
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引用次数: 0

摘要

GCOC 是一种片上基因组分类系统 (SoC),它通过 k-mer 匹配对基因组进行分类,这种方法是将 DNA 查询序列分成一组大小为 k 的短 DNA 片段,然后在参考基因组数据库中进行搜索,其基本假设是同一生物体(或其近似变种)的 DNA 测序读数共享这些 k-mer 中的大部分。GCOC 的核心是一个具有相似性或近似搜索能力的内容寻址存储器(SAS-CAM),它除了支持精确匹配外,还支持近似搜索或汉明距离容差搜索。分类操作由嵌入式 RISC-V 处理器控制。GCOC 分类平台采用 65 纳米商用工艺设计和制造。我们通过硅测量对 GCOC 的分类效率及其性能、硅面积和功耗进行了全面分析。GCOC 每秒可分类 769.2K 个短 DNA 读数。GCOC SoC 的硅面积为 3.12 平方毫米,功耗为 1.27 毫瓦。我们设想将 GCOC 部署为现场(例如医疗点)便携式分类器,要求分类实时、操作简便且节能。
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GCOC: A Genome Classifier-On-Chip based on Similarity Search Content Addressable Memory.

GCOC is a genome classification system-on-chip (SoC) that classifies genomes by k-mer matching, an approach that divides a DNA query sequence into a set of short DNA fragments of size k, which are searched in a reference genome database, with the underlying assumption that sequenced DNA reads of the same organism (or its close variants) share most of such k-mers. At the core of GCOC is a similarity, or approximate search-capable Content Addressable Memory (SAS-CAM), which in addition to exact match, also supports approximate, or Hamming distance tolerant search. Classification operation is controlled by an embedded RISC-V processor. GCOC classification platform was designed and manufactured in a commercial 65nm process. We conduct a thorough analysis of GCOC classification efficiency as well as its performance, silicon area, and power consumption using silicon measurements. GCOC classifies 769.2K short DNA reads/sec. The silicon area of GCOC SoC is 3.12mm2 and its power consumption is 1.27mW. We envision GCOC deployed as a field (for example at points of care) portable classifier where the classification is required to be real-time, easy to operate and energy efficient.

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Table of Contents Erratum to “Design of an Extreme Low Cutoff Frequency Highpass Frontend for CMOS ISFET via Direct Tunneling Principle” IEEE Transactions on Biomedical Circuits and Systems Publication Information IEEE Circuits and Systems Society Information Guest Editorial: Ultralow-Power Technologies for Edge Computing in Human-Machine Interface Applications
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