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Resistive-switching random-access memory (RRAM) offers efficient in-memory computing (IMC) by minimizing data movement. This study explores RRAM materials, devices, and circuits for advanced AI applications, addressing challenges for low-power computing.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Electrical Engineering

Background:

  • The information age demands novel hardware for efficient data storage and processing.
  • Conventional von Neumann architecture faces energy consumption challenges due to data movement.
  • In-memory computing (IMC) paradigms minimize data movement for enhanced energy efficiency and performance.

Purpose of the Study:

  • To explore resistive-switching random-access memory (RRAM) as a key technology for in-memory computing (IMC).
  • To investigate RRAM from materials, device, circuit, and application perspectives.
  • To highlight RRAM's potential for memory and computing applications, including AI.

Main Methods:

  • Review of RRAM materials and device properties relevant to storage and computing.
  • Analysis of circuit implementations for AI models using RRAM.
  • Examination of RRAM-based demonstrators for memory and IMC applications.

Main Results:

  • RRAM exhibits excellent scalability and nonvolatile storage, making it suitable for IMC.
  • Specialized RRAM device engineering is crucial for implementing modern AI models.
  • Demonstrators show RRAM's potential in embedded nonvolatile memory (eNVM) and AI accelerators.

Conclusions:

  • RRAM is a promising technology for low-power, sustainable AI and efficient data handling.
  • Further RRAM device engineering is needed to overcome challenges in developing advanced computing systems.
  • RRAM's integration in memory and computing applications can significantly reduce energy consumption.