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存内计算芯片研究进展及应用

郭昕婕 王光燿 王绍迪

郭昕婕, 王光燿, 王绍迪. 存内计算芯片研究进展及应用[J]. 电子与信息学报, 2023, 45(5): 1888-1898. doi: 10.11999/JEIT220420
引用本文: 郭昕婕, 王光燿, 王绍迪. 存内计算芯片研究进展及应用[J]. 电子与信息学报, 2023, 45(5): 1888-1898. doi: 10.11999/JEIT220420
GUO Xinjie, WANG Guangyao, WANG Shaodi. Technology Developments and Applications of In-memory Computing Processors[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1888-1898. doi: 10.11999/JEIT220420
Citation: GUO Xinjie, WANG Guangyao, WANG Shaodi. Technology Developments and Applications of In-memory Computing Processors[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1888-1898. doi: 10.11999/JEIT220420

存内计算芯片研究进展及应用

doi: 10.11999/JEIT220420
基金项目: 科技部“科技助力经济2020”重点专项项目(SQ2020YFF0404823)
详细信息
    作者简介:

    郭昕婕:女,博士,研究方向为存算一体芯片设计

    王光燿:男,硕士生,研究方向为存算一体芯片设计

    王绍迪:男,博士,研究方向为存储器及存算一体架构设计

    通讯作者:

    王绍迪 shaodi.wang@witintech.com

  • 中图分类号: TN4

Technology Developments and Applications of In-memory Computing Processors

Funds: The Ministry of Science and Technology's Key Special Project (SQ2020YFF0404823)
  • 摘要: 随着数据快速增长,冯诺依曼架构内存墙成为计算性能进一步提升的关键瓶颈。新型存算一体架构(包括存内计算(IMC)架构与近存计算(NMC)架构),有望打破冯诺依曼架构瓶颈,大幅提高算力和能效。该文介绍了存算一体芯片的发展历程、研究现状以及基于各类存储器介质(如传统存储器DRAM, SRAM和Flash和新型非易失性存储器ReRAM, PCM, MRAM, FeFET等)的存内计算基本原理、优势与面临的问题。然后,以知存科技WTM2101量产芯片为例,重点介绍了存算一体芯片的电路结构与应用现状。最后,分析了存算一体芯片未来的发展前景与面临的挑战。
  • 图  1  计算架构的演变示意图

    图  2  基于不同存储介质的计算架构演变图[39]

    图  3  基于SRAM的存内计算单元结构

    图  4  基于DRAM的存内计算基本原理[43]

    图  5  基于ReRAM的存内计算阵列结构与测试芯片

    图  6  基于MRAM的存内计算阵列布局图、显微图和结构[49]

    图  7  基于NOR Flash的存内计算技术原理与相关产品

    图  8  WTM2101的阵列结构与芯片架构

    图  9  WTM2101存内计算芯片8 bit精度运算测试结果

    图  10  搭载WTM2101的耳机产品与WTM2101自动化部署流程

    图  11  WTM2101降噪性能图

    表  1  基于不同存储介质的存内计算芯片性能比较

    标准SRAMDRAMFlashReRAMPCMFeFETMRAM
    非易失性
    多比特存储能力
    面积效率一般
    功耗效率
    工艺微缩性较差较好
    成本较高较低
    技术成熟度测试芯片测试芯片量产产品测试芯片测试芯片器件测试芯片
    下载: 导出CSV

    表  2  神经网络的累计余弦相似度

    神经网络累计余弦相似度
    第0层0.993
    第1层0.996
    第2层0.997
    第3层0.998
    第4层0.998
    第5层0.997
    第6层0.994
    整个神经网络0.994
    下载: 导出CSV

    表  3  WTM2101与市场同类产品的性能比较

    标准市场现有同类产品WTM2101
    算力复杂度(Mops)功耗(mA)算力复杂度(Mops)功耗(mA)
    语音激活检测0.10.10.10.07
    语音唤醒200.64000.4
    40命令词识别3024000.6
    100命令词识别100104000.8
    环境去噪150158001
    声纹识别150015015002
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-08
  • 修回日期:  2022-10-09
  • 网络出版日期:  2022-10-20
  • 刊出日期:  2023-05-10

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