基于闪存存储的近数据处理技术综述
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金项目(61802038, 62072059)


A Survey of Flash Memory Based Near-Data Processing Technology
Author:
Affiliation:

Fund Project:

National Natural Science Foundation of China (61802038, 62072059)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    在冯• 诺依曼架构中,存储与计算的分离造成了存储墙问题,导致现有的系统架构难以应对大数据和人工智能时代的数据爆炸。数据的持续增长导致了计算范式的变化,研究者们开始尝试将计算单元移动到存储器中,即近数据处理技术。近数据处理技术是指利用存储控制器的计算能力,执行与数据存取紧密相关的任务,在减少数据迁移的同时,具有低延迟、高可扩展性和低功耗等优点,具有广阔的应用前景。该文首先介绍了近数据处理系统的架构,其次针对特定应用和面向通用场景的相关研究成果进行概述,并总结了软硬件平台和产业进展,最后展望了其未来的发展趋势。

    Abstract:

    The isolation of storage and compute units in the Von Neumann architecture leads to the “storage wall” problem, which makes the existing system architecture hard to cope with the challenges of data explosion caused by the wide application of big data and artificial intelligence technologies. The continuous growth of data has led to an evolution in the computing paradigm. Researchers try to move the compute unit to the storage system, that is Near-Data Processing (NDP) technology. NDP technology refers to utilizing the computing power of the storage controller to perform I/O intensive computing tasks, which brings advantages such as low latency, high scalability, and low power consumption while reducing data movement, and has broad application prospects. This article first introduces the near-data computing architecture, subsequently outlines the research results of NDP systems for specific applications and some general scenarios, then summarizes the hardware and software platform and industry progress of NDP, finally looks into the future development trend of NDP technology.

    参考文献
    相似文献
    引证文献
引用本文

引文格式
李迦雳,刘铎,陈咸彰,等.基于闪存存储的近数据处理技术综述 [J].集成技术,2022,11(3):23-41

Citing format
LI Jiali, LIU Duo, CHEN Xianzhang, et al. A Survey of Flash Memory Based Near-Data Processing Technology[J]. Journal of Integration Technology,2022,11(3):23-41

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2022-05-18
  • 出版日期:
文章二维码