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
{"title":"GCOC:基于相似性搜索内容可寻址内存的片上基因组分类器。","authors":"Yuval Harary, Paz Snapir, Shir Siman Tov, Chen Kruphman, Eyal Rechef, Zuher Jahshan, Esteban Garzon, Leonid Yavits","doi":"10.1109/TBCAS.2024.3449788","DOIUrl":null,"url":null,"abstract":"<p><p>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 3.12mm<sup>2</sup> 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.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GCOC: A Genome Classifier-On-Chip based on Similarity Search Content Addressable Memory.\",\"authors\":\"Yuval Harary, Paz Snapir, Shir Siman Tov, Chen Kruphman, Eyal Rechef, Zuher Jahshan, Esteban Garzon, Leonid Yavits\",\"doi\":\"10.1109/TBCAS.2024.3449788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 3.12mm<sup>2</sup> 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.</p>\",\"PeriodicalId\":94031,\"journal\":{\"name\":\"IEEE transactions on biomedical circuits and systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on biomedical circuits and systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TBCAS.2024.3449788\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on biomedical circuits and systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TBCAS.2024.3449788","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network with a Diamond Topology for Real-Time Data Processing. An Electrochemical CMOS Biosensor Array Using Phase-Only Modulation With 0.035% Phase Error And In-Pixel Averaging. GCOC: A Genome Classifier-On-Chip based on Similarity Search Content Addressable Memory. Low-Power and Low-Cost AI Processor with Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection. Parallel Resonant Magnetic Field Generator for Biomedical Applications.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1