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An Energy-Efficient Computing-in-Memory (CiM) Scheme Using Field-Free Spin-Orbit Torque (SOT) Magnetic RAMs

  • Bi Wu*
  • , Haonan Zhu
  • , Dayane Reis
  • , Zhaohao Wang
  • , Ying Wang
  • , Ke Chen
  • , Weiqiang Liu*
  • , Fabrizio Lombardi
  • , Xiaobo Sharon Hu
  • *Corresponding author for this work
  • Nanjing University of Aeronautics and Astronautics
  • Ministry of Industry and Information Technology
  • University of Notre Dame
  • University of South Florida
  • CAS - Institute of Computing Technology
  • Northeastern University

Research output: Contribution to journalArticlepeer-review

Abstract

The separation of memory and computing units in the von Neumann architecture leads to undesirable energy consumption due to data movement and insufficient memory bandwidth. Energy-efficient in-memory computing platforms have the potential to address such issues. Due to its non-volatility and advantageous features over CMOS (such as low power, near-zero leakage current and high integration density), spin-based devices have been advocated for in-memory computing. This paper proposes a field-free Spin Orbit Torque (FF-SOT) MRAM based computing-in-memory (CiM) scheme that realizes XNOR/XOR logic and a cascading adder. This novel FF-SOT-CiM design does not require expensive peripheral circuits for computation while using the same memory cell design as a SOT-MRAM. Furthermore, FF-SOT-CiM does not require additional write cycles to save the result of its computations in the memory. The design offers higher write speed and; lower operating energy compared to CiM schemes based on other technologies; it also alleviates the source degeneration effect by leveraging an advanced switching mechanism. Extensive simulation results show that the proposed FF-SOT-CiM achieves up to 3.1x (2.6x) latency (energy) reduction compared to SRAM-based CiM, with negligible hardware overhead when performing in-memory XOR. ADD operations; the proposed FF-SOT-CiM can be to 5.0X and 1.5X faster and 3.4X and 1.1X more energy efficient than existing STT-based and FeFET-based schemes, respectively.

Original languageEnglish
Pages (from-to)331-342
Number of pages12
JournalIEEE Transactions on Emerging Topics in Computing
Volume11
Issue number2
DOIs
StatePublished - 1 Apr 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Computing-in-memory
  • full adder
  • magnetic random access memory
  • spin orbit torque (SOT)
  • XNOR/XOR

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