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Error detection and correction in content addressable memories by using bloom filters

机译:使用布隆过滤器对内容可寻址存储器进行错误检测和纠正

摘要

A content addressable memory (CAM) is an SRAM-based memory that can be accessed in parallel to search for a given search word, providing as a result the address of the matching data. Like conventional memories, a CAM can be affected by the occurrence of single event upsets (SEUs) that can alter the content of one of more memory cells causing different effects such as pseudo-HIT or pseudo-MISS events. It is well known that, because of the parallel search performed by a CAM during the query of a word, a standard error correction code could not defend it against SEU events. In this paper, we propose a method that does not require any modification to a CAM’s internal structure and, therefore, can be easily applied at system level. Error detection is performed by using a probabilistic structure called “Bloom filter,” which can signal if a given data is present in the CAM. Bloom filters permit to efficiently store and query the presence of data in a set. But, while a CAM suffers from SEU induced errors, the probabilistic nature of Bloom filters has as a consequence the so called false-positive effect. This paper shows that, by combining the use of a Bloom filter with a CAM, the complementary limitations of these modules can be compensated. The combined use of a CAM and a Bloom filter is analyzed in different cases, showing that the proposed technique can be implemented with a low penalty in terms of area and power consumption.
机译:内容可寻址存储器(CAM)是基于SRAM的存储器,可以并行访问该存储器以搜索给定的搜索词,从而提供匹配数据的地址。像常规存储器一样,CAM可能会受到单事件翻转(SEU)的影响,单事件翻转会改变一个或多个存储单元中一个单元的内容,从而导致不同的影响,例如伪HIT或伪MISS事件。众所周知,由于在单词查询期间由CAM执行并行搜索,因此标准纠错码无法针对SEU事件保护它。在本文中,我们提出了一种无需修改CAM内部结构的方法,因此可以轻松地在系统级应用。错误检测是通过使用称为“布鲁姆滤波器”的概率结构执行的,该结构可以发出信号通知CAM中是否存在给定数据。布隆过滤器允许有效地存储和查询集合中数据的存在。但是,尽管CAM遭受SEU引起的误差,但Bloom滤波器的概率性质因此具有所谓的假阳性效应。本文表明,通过结合使用布鲁姆(Bloom)滤波器和CAM,可以补偿这些模块的互补限制。在不同情况下分析了CAM和Bloom滤波器的组合使用,表明所提出的技术可以在面积和功耗方面以较低的代价实现。

著录项

  • 作者

    Pontarelli S; Ottavi M;

  • 作者单位
  • 年度 2013
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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