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Cited by 89Advanced Memory and Neural ComputingMagnetic properties of thin filmsLow-power high-performance VLSI design

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A Multilevel Cell STT-MRAM-Based Computing In-Memory Accelerator for Binary Convolutional Neural Network

Pan, Yu, Ouyang, Peng, Zhao, Yinglin, Kang, Wang, Yin, Shouyi, Zhang, Youguang, Zhao, Weisheng, Wei, Shaojun·2018·IEEE Transactions on Magnetics
DOI 10.1109/tmag.2018.2848625
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Abstract

Due to additive operation’s dominated computation and simplified network in binary convolutional neural network (BCNN), it is promising for Internet of Things scenarios which demand ultralow power consumption. By means of fully exploiting the in-memory computing advantages and low current consumption design using multilevel cell (MLC) spin-toque transfer magnetic random access memory (STT-MRAM), this paper proposes an MLC-STT-computing in-memory-based computing in-memory architecture to achieve convolutional operation for BCNN to further reduce the power consumption. Simulation results show that compared with the resistive random access memory (RRAM)- and spin orbit torque-STT-MRAM-based counterparts, the architecture proposed in this paper reduces power consumption by ~$35{\times}$and 59% in Modified National Institute of Standards and Technology data set, respectively.

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Research works printed but not linked: 11

  1. 10.1145/1555815.1555758 · DOI
  2. 10.1109/ASPDAC.2017.7858419 · DOI
  3. 10.1109/MICRO.2007.33 · DOI
  4. 10.1109/TED.2011.2182053 · DOI
  5. 10.1109/TCAD.2012.2185930 · DOI
  6. 10.1145/2333660.2333664 · DOI
  7. jain, 2017, Computing in memory with spin-transfer torque magnetic RAM
  8. 10.1145/2429384.2429498 · DOI
  9. courbariaux, 2016, Binarized neural networks Training deep neural networks with weights and activations constrained to +1 or ?1
  10. 10.1109/ASPDAC.2018.8297291 · DOI
  11. rastegari, 2016, Xnor-net Imagenet classification using binary convolutional neural networks

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Metadata

Authors
Pan, Y., Ouyang, P., Zhao, Y., Kang, W., Yin, S., Zhang, Y., Zhao, W., & Wei, S.
Affiliations
  • Beihang University (CN)
  • Tsinghua University (CN)
ORCID
Venue
IEEE Transactions on Magnetics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018-11
Volume
54
Issue
11
Pages
1-5
ISSN
  • 0018-9464 (ISSN-L)
  • 1941-0069
DOI
10.1109/tmag.2018.2848625
OpenAlex
W2812853021
Cited by
89
References
0
Citations by year
2026: 2 · 2025: 7 · 2024: 13 · 2023: 12 · 2022: 20 · 2021: 20 · 2020: 6 · 2019: 8 · 2018: 1
FWCI
3.35
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
IEEE Transactions on Magnetics (publishedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
  • Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Magnetic properties of thin films
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
Keywords
spin-transfer torque memory, in-memory computing, binary convolutional neural networks, energy-efficient computing, Internet of Things, energy consumption reduction

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CoChat. (2026). Citation check: A Multilevel Cell STT-MRAM-Based Computing In-Memory Accelerator for Binary Convolutional Neural Network. https://app.cochat.ai/papers/10.1109/tmag.2018.2848625
The paper
Pan, Y., Ouyang, P., Zhao, Y., Kang, W., Yin, S., Zhang, Y., Zhao, W., & Wei, S. (2018). A Multilevel Cell STT-MRAM-Based Computing In-Memory Accelerator for Binary Convolutional Neural Network. IEEE Transactions on Magnetics. https://doi.org/10.1109/tmag.2018.2848625