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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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Toward memristive in-memory computing: principles and applications.

Han Bao1, Houji Zhou1, Jiancong Li1

  • 1School of Integrated Circuits, School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan, 430074, China.

Frontiers of Optoelectronics
|January 13, 2023
PubMed
Summary
This summary is machine-generated.

Memristive in-memory computing offers a solution to data communication bottlenecks in big data processing. This review surveys its applications in soft and hard computing, highlighting future opportunities in the Artificial Intelligence of Things.

Keywords:
Digital image processingIn-memory computingMachine learningMatrix–vector multiplicationMemristorScientific computing

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Area of Science:

  • Computer Science
  • Materials Science
  • Electrical Engineering

Background:

  • Traditional von Neumann architecture faces challenges with data communication costs due to separated processing units and memory.
  • The rapid growth of big data exacerbates these communication bottlenecks.
  • Memristive in-memory computing emerges as a promising paradigm to overcome these limitations.

Purpose of the Study:

  • To provide a comprehensive survey of recent advances in memristive in-memory computing applications.
  • To categorize applications into soft computing and hard computing types.
  • To discuss future trends and challenges for memristive in-memory computing.

Main Methods:

  • Literature review of recent research on memristive in-memory computing.
  • Categorization of applications based on computational requirements (soft vs. hard computing).
  • Analysis of hardware solutions and computational accuracies for different application types.

Main Results:

  • Memristive in-memory computing has demonstrated significant potential in both soft computing (e.g., artificial neural networks, machine learning) and hard computing (e.g., scientific computing, digital image processing).
  • Different application types necessitate distinct hardware solutions and computational accuracy levels.
  • The paradigm shows promise for addressing the limitations of traditional computing architectures.

Conclusions:

  • Memristive in-memory computing is a key technology for future computing paradigms, particularly for big data and AI.
  • Further research is needed to address remaining challenges and fully realize its potential.
  • The integration of memristive in-memory computing is crucial for the advancement of the Artificial Intelligence of Things.