Sparse and Robust RRAM-based Efficient In-memory Computing for DNN Inference

被引：0
|
作者：
Meng, Jian [1 ]
Yeo, Injune [1 ]
Yang, Li [1 ]
Fan, Deliang [1 ]
Seo, Jae-sun [1 ]
Yu, Shimeng [2 ]
Shim, Wonbo [2 ,3 ]
机构：
[1] Arizona State Univ, Tempe, AZ 85287 USA
[2] Georgia Inst Technol, Atlanta, GA 30332 USA
[3] Seoul Natl Univ Sci & Technol, Seoul, South Korea
来源：
2022 IEEE INTERNATIONAL RELIABILITY PHYSICS SYMPOSIUM (IRPS) | 2022年
关键词：
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
3C.1
引用
收藏
页数：1
相关论文
共 50 条
  • [41] Efficient and Robust RRAM-Based Convolutional Weight Mapping With Shifted and Duplicated Kernel
    Zhang, Yuhang
    He, Guanghui
    Wang, Guoxing
    Li, Yongfu
    IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2021, 40 (02) : 287 - 300
  • [42] Monolithically Integrated RRAM- and CMOS-Based In-Memory Computing Optimizations for Efficient Deep Learning
    Yin, Shihui
    Kim, Yulhwa
    Han, Xu
    Barnaby, Hugh
    Yu, Shimeng
    Luo, Yandong
    He, Wangxin
    Sun, Xiaoyu
    Kim, Jae-Joon
    Seo, Jae-sun
    IEEE MICRO, 2019, 39 (06) : 54 - 63
  • [43] An isolated symmetrical 2T2R cell enabling high precision and high density for RRAM-based in-memory computing
    Ling, Yaotian
    Wang, Zongwei
    Yang, Yuhang
    Bao, Lin
    Bao, Shengyu
    Wang, Qishen
    Cai, Yimao
    Huang, Ru
    SCIENCE CHINA-INFORMATION SCIENCES, 2024, 67 (05)
  • [44] In-Memory Computing Architectures for Sparse Distributed Memory
    Kang, Mingu
    Shanbhag, Naresh R.
    IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2016, 10 (04) : 855 - 863
  • [45] Experimental demonstration of Single-Level and Multi-Level-Cell RRAM-based In-Memory Computing with up to 16 parallel operations
    Esmanhotto, E.
    Hirtzlin, T.
    Castellani, N.
    Martin, S.
    Giraud, B.
    Andrieu, F.
    Nodin, J. F.
    Querlioz, D.
    Portal, J-M.
    Vianello, E.
    2022 IEEE INTERNATIONAL RELIABILITY PHYSICS SYMPOSIUM (IRPS), 2022,
  • [46] An isolated symmetrical 2T2R cell enabling high precision and high density for RRAM-based in-memory computing
    Yaotian LING
    Zongwei WANG
    Yuhang YANG
    Lin BAO
    Shengyu BAO
    Qishen WANG
    Yimao CAI
    Ru HUANG
    Science China(Information Sciences), 2024, 67 (05) : 287 - 294
  • [47] Structured Pruning of RRAM Crossbars for Efficient In-Memory Computing Acceleration of Deep Neural Networks
    Meng, Jian
    Yang, Li
    Peng, Xiaochen
    Yu, Shimeng
    Fan, Deliang
    Seo, Jae-Sun
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2021, 68 (05) : 1576 - 1580
  • [48] A Coordinated Model Pruning and Mapping Framework for RRAM-Based DNN Accelerators
    Qu, Songyun
    Li, Bing
    Zhao, Shixin
    Zhang, Lei
    Wang, Ying
    IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2023, 42 (07) : 2364 - 2376
  • [49] One-Transistor-Multiple-RRAM Cells for Energy-Efficient In-Memory Computing
    Uhlmann, Max
    Quesada, Emilio Perez-Bosch
    Fritscher, Markus
    Perez, Eduardo
    Schubert, Markus Andreas
    Reichenbach, Marc
    Ostrovskyy, Philip
    Wenger, Christian
    Kahmen, Gerhard
    2023 21ST IEEE INTERREGIONAL NEWCAS CONFERENCE, NEWCAS, 2023,
  • [50] Benchmarking DNN Mapping Methods for the in-Memory Computing Accelerators
    Wang, Yimin
    Fong, Xuanyao
    IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, 2023, 13 (04) : 1040 - 1051
12345