Reliable In-Memory Neuromorphic Computing Using Spintronics

Recently Spin Transfer Torque Random Access Memory (STT-MRAM) technology has drawn a lot of attention for the direct implementation of neural networks, because it offers several advantages such as near-zero leakage, high endurance, good scalability, small foot print and CMOS compatibility. The stori...

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Veröffentlicht in:2019 24th Asia and South Pacific Design Automation Conference (ASP-DAC) S. 1 - 7
Hauptverfasser: Munch, Christopher, Bishnoi, Rajendra, Tahoori, Mehdi B.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 21.01.2019
Schlagworte:
Biological neural networks
defect modeling
Fabrication
MTJ
neuromorphic computing
Neuromorphic engineering
Neurons
Redundancy
Scalability
Torque
ISSN:2153-697X
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Abstract Recently Spin Transfer Torque Random Access Memory (STT-MRAM) technology has drawn a lot of attention for the direct implementation of neural networks, because it offers several advantages such as near-zero leakage, high endurance, good scalability, small foot print and CMOS compatibility. The storing device in this technology, the Magnetic Tunnel Junction (MTJ), is developed using magnetic layers that requires new fabrication materials and processes. Due to complexities of fabrication steps and materials, MTJ cells are subject to various failure mechanisms. As a consequence, the functionality of the neuromorphic computing architecture based on this technology is severely affected. In this paper, we have developed a framework to analyze the functional capability of the neural network inference in the presence of the several MTJ defects. Using this framework, we have demonstrated the required memory array size that is necessary to tolerate the given amount of defects and how to actively decrease this overhead by disabling parts of the network.
AbstractList Recently Spin Transfer Torque Random Access Memory (STT-MRAM) technology has drawn a lot of attention for the direct implementation of neural networks, because it offers several advantages such as near-zero leakage, high endurance, good scalability, small foot print and CMOS compatibility. The storing device in this technology, the Magnetic Tunnel Junction (MTJ), is developed using magnetic layers that requires new fabrication materials and processes. Due to complexities of fabrication steps and materials, MTJ cells are subject to various failure mechanisms. As a consequence, the functionality of the neuromorphic computing architecture based on this technology is severely affected. In this paper, we have developed a framework to analyze the functional capability of the neural network inference in the presence of the several MTJ defects. Using this framework, we have demonstrated the required memory array size that is necessary to tolerate the given amount of defects and how to actively decrease this overhead by disabling parts of the network.
Author Bishnoi, Rajendra
Tahoori, Mehdi B.
Munch, Christopher
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  givenname: Christopher
  surname: Munch
  fullname: Munch, Christopher
  email: christopher.muench@kit.edu
  organization: Karlsruhe Institute of Technology (KIT),Chair of Dependable Nano Computing (CDNC),Karlsruhe,Germany
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  givenname: Rajendra
  surname: Bishnoi
  fullname: Bishnoi, Rajendra
  email: rajendra.bishnoi@kit.edu
  organization: Karlsruhe Institute of Technology (KIT),Chair of Dependable Nano Computing (CDNC),Karlsruhe,Germany
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  givenname: Mehdi B.
  surname: Tahoori
  fullname: Tahoori, Mehdi B.
  email: mehdi.tahoori@kit.edu
  organization: Karlsruhe Institute of Technology (KIT),Chair of Dependable Nano Computing (CDNC),Karlsruhe,Germany
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Snippet Recently Spin Transfer Torque Random Access Memory (STT-MRAM) technology has drawn a lot of attention for the direct implementation of neural networks, because...
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SubjectTerms Biological neural networks
defect modeling
Fabrication
MTJ
neuromorphic computing
Neuromorphic engineering
Neurons
Redundancy
Scalability
Torque
Title Reliable In-Memory Neuromorphic Computing Using Spintronics
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