Domain Wall Memory-Based Design of Deep Neural Network Convolutional Layers

Jinil Chung, Woong Choi, Jongsun Park, Swaroop Ghosh

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In the hardware implementation of deep learning algorithms such as, convolutional neural networks (CNNs) and binarized neural networks (BNNs), multiple dot products and memories for storing parameters take a significant portion of area and power consumption. In this paper, we propose a domain wall memory (DWM) based design of CNN and BNN convolutional layers. In the proposed design, the resistive cell sensing mechanism is efficiently exploited to design low-cost DWM-based cell arrays for storing parameters. The unique serial access mechanism and small footprint of DWM are also used to reduce the area and energy cost of DWM-based design for filter sliding. Simulation results with 65 nm CMOS process show 45% and 43% of energy savings compared to the conventional CNN and BNN design approach, respectively.

Original languageEnglish
Article number8963965
Pages (from-to)19783-19798
Number of pages16
JournalIEEE Access
Publication statusPublished - 2020


  • Binarized neural network
  • convolutional neural network
  • deep neural network
  • domain wall memory

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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