In-Memory Computing Architecture for a Convolutional Neural Network Based on Spin Orbit Torque MRAM

Jun Ying Huang, Jing Lin Syu, Yao Tung Tsou*, Sy Yen Kuo, Ching Ray Chang

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

2 Scopus citations

Abstract

Recently, numerous studies have investigated computing in-memory (CIM) architectures for neural networks to overcome memory bottlenecks. Because of its low delay, high energy efficiency, and low volatility, spin-orbit torque magnetic random access memory (SOT-MRAM) has received substantial attention. However, previous studies used calculation circuits to support complex calculations, leading to substantial energy consumption. Therefore, our research proposes a new CIM architecture with small peripheral circuits; this architecture achieved higher performance relative to other CIM architectures when processing convolution neural networks (CNNs). We included a distributed arithmetic (DA) algorithm to improve the efficiency of the CIM calculation method by reducing the excessive read/write times and execution steps of CIM-based CNN calculation circuits. Furthermore, our method also uses SOT-MRAM to increase the calculation speed and reduce power consumption. Compared with CIM-based CNN arithmetic circuits in previous studies, our method can achieve shorter clock periods and reduce read times by up to 43.3% without the need for additional circuits.

Original languageEnglish
Article number1245
JournalElectronics (Switzerland)
Volume11
Issue number8
DOIs
StatePublished - 01 04 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • computing in memory
  • convolution neural network
  • distributed arithmetic
  • MRAM
  • processing in memory
  • SOT-MRAM

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