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    高鹏, 汪东升, 王海霞. 采用流水化伪随机编码算法的相变存储器寿命延长方法[J]. 计算机研究与发展, 2017, 54(6): 1357-1366. DOI: 10.7544/issn1000-1239.2017.20170065
    引用本文: 高鹏, 汪东升, 王海霞. 采用流水化伪随机编码算法的相变存储器寿命延长方法[J]. 计算机研究与发展, 2017, 54(6): 1357-1366. DOI: 10.7544/issn1000-1239.2017.20170065
    Gao Peng, Wang Dongsheng, Wang Haixia. Increasing PCM Lifetime by Using Pipelined Pseudo-Random Encoding Algorithm[J]. Journal of Computer Research and Development, 2017, 54(6): 1357-1366. DOI: 10.7544/issn1000-1239.2017.20170065
    Citation: Gao Peng, Wang Dongsheng, Wang Haixia. Increasing PCM Lifetime by Using Pipelined Pseudo-Random Encoding Algorithm[J]. Journal of Computer Research and Development, 2017, 54(6): 1357-1366. DOI: 10.7544/issn1000-1239.2017.20170065

    采用流水化伪随机编码算法的相变存储器寿命延长方法

    Increasing PCM Lifetime by Using Pipelined Pseudo-Random Encoding Algorithm

    • 摘要: 相变存储器(phase change memory, PCM)是一种颇具前景的新型存储器件,具有非易失性、静态功耗低和存储密度高的优点.然而,该类器件的低写入寿命是其在实用化中亟待克服的关键问题之一.一般来说,通过每次写入时仅写入相异位的策略,可以减少产生的平均写入量,从而延长PCM的写入寿命.然而,应用这一差异式的写入策略通常又会以降低读写速度为代价.为此,提出了一种兼具高效和快速特点的写入量减少方法FEBRE(a fast and efficient bit-flipping reduction technique to extend PCM lifetime).该方法在差分写入阶段前,设计并使用了一种快速的一对多映射,将待写入的数据并行映射为多个编码向量,从而增加了从其中找到一个与已有数据最近的向量的可能性.此外,还提出了一种流水化的伪随机编码算法,用以加速一对多映射中的编码过程,从而降低写入开销.实验表明,与目前领先的PRES(pseudo-random encoding scheme)方法相比,FEBRE方法在写入操作中,平均减少了5%以上的写入量,提升了2倍以上的编码速度;在读取操作中,减少了45%以上的解码操作次数.

       

      Abstract: Phase change memory (PCM) is a promising technique due to its low static power, non-volatility, and density potential. However, the low endurance remains as the key problem to be solved before it can be widely used in practice. Generally, minimizing modified bits in write operation by writing the different bits, is an effective method to extend the lifetime of PCM. But it’s still challenging to reach the minimum without causing significant slowdown of read/write operations. To this end, we propose FEBRE: A fast and efficient bit-flipping reduction technique to extend PCM lifetime. The key idea of our method is to design and use a novel one-to-many parallel mapping before differential write stage. Specifically, FEBRE employs a new data encoding method to generate multiple highly random distributed encoded vectors from one writing data item, which thus increases the possibility of identifying the nearest one to stored data in those vectors. The other contribution of our technique is a pipelined pseudo-random encoding algorithm (PPREA). The new algorithm reduces writing overhead because it is able to accelerate the procedure of the one-to-many mapping. The experiment shows that our technique, compared with PRES, can reduce bit flips by 5.31% on average, and improve the encodingdecoding speed by 2.29x and 45%, respectively.

       

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