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基于超平面树的数据挖掘,使用多功能记忆交叉条数组.

Sunwoo Cheong1, Dong Hoon Shin1, Soo Hyung Lee1

  • 1Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea. cheolsh@snu.ac.kr.

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概括
此摘要是机器生成的。

本研究介绍了一种基于memristor的新方法,用于高效的数据挖掘,利用随机和二进制切换模式来检测异常值和数据聚类. 这种方法可节省大量能源,性能与传统计算相提并论.

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 计算机科学 计算机科学
  • 电气工程 电气工程

背景情况:

  • 记忆器件为内存计算提供了独特的特性.
  • 传统的数据挖掘算法在能源效率和速度方面面临着挑战.
  • 开发新的硬件加速算法对于大数据处理至关重要.

研究的目的:

  • 探索Ta/HfO2/RuO2记忆器的随机和二进制切换行为.
  • 实施一个结合的数据挖掘方法,用于异常值检测和数据聚类,使用记忆性横条数组.
  • 评估拟议的基于memristor的方法的性能,时间复杂性和能源效率.

主要方法:

  • 使用随机切换模式来并行生成用于数据压缩的随机超平面.
  • 采用二进制切换模式进行并行汉明距离计算,以测量数据相似性.
  • 实现基于少数的异常值检测方法和修改的K-means集群算法在一个memristive横条数组上.
  • 进行阵列测量和硬件模拟以分析超参数影响.

主要成果:

  • 随机模式有效地压缩空间信息,同时保留关键特征.
  • 二进制模式通过哈明距离计算实现了高效的相似度测量.
  • 综合方法成功实现了异常值检测和数据聚类,具有高分类性能.
  • 拟议的方法具有线性时间复杂度O (n) 和与传统数字算法相比,消耗的能量<1%.

结论:

  • /HfO2/RuO2记忆器可以有效地用于联合数据挖掘任务.
  • 拟议的基于memristor的数据挖掘方法提供了显著的能源效率和与软件解决方案相匹配的性能.
  • 这项工作表明了开发用于大数据分析的低功耗,高性能神经形态计算系统的有希望的方向.