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Memristive and CMOS Devices for Neuromorphic Computing.

Valerio Milo1, Gerardo Malavena1, Christian Monzio Compagnoni1

  • 1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and Italian Universities Nanoelectronics Team (IU.NET), Piazza L. da Vinci 32, 20133 Milano, Italy.

Materials (Basel, Switzerland)
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PubMed
Summary
This summary is machine-generated.

Neuromorphic computing mimics the brain for energy-efficient processing. This study reviews complementary metal-oxide semiconductor (CMOS) and memristive devices for advanced artificial neural networks.

Keywords:
Flash memoriesartificial neural networkmemristive devicesneuromorphic computingpattern recognitionresistive switchingspiking neural networksynaptic plasticity

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Area of Science:

  • Computer Science
  • Materials Science
  • Neuroscience

Background:

  • Conventional von Neumann architecture faces limitations in energy efficiency and processing power.
  • The human brain offers a highly efficient and compact model for computation.
  • Neuromorphic computing aims to replicate brain-like processing for enhanced performance.

Purpose of the Study:

  • To provide an overview of promising device concepts for neuromorphic computing.
  • To discuss the potential of complementary metal-oxide semiconductor (CMOS) and memristive technologies.
  • To highlight challenges and future perspectives in neuromorphic device development.

Main Methods:

  • Review of CMOS-based floating-gate memory devices for artificial neural networks.
  • Discussion of various memristive device concepts for deep and spiking neural networks.
  • Analysis of technological challenges and future outlooks in neuromorphic computing.

Main Results:

  • CMOS and memristive technologies show significant promise for neuromorphic applications.
  • Floating-gate devices are suitable for artificial neural networks.
  • Memristive devices offer potential for deep and spiking neural network architectures.

Conclusions:

  • Novel device concepts are crucial for achieving brain-like scalability and low-power operation.
  • Both CMOS and memristive technologies are key enablers for future neuromorphic systems.
  • Overcoming technological challenges will pave the way for advanced neuromorphic computing.