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Memory devices and applications for in-memory computing.

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In-memory computing offers a non-von Neumann approach to overcome data movement bottlenecks in artificial intelligence applications. This review explores memory devices enabling in-memory computing for diverse scientific and machine learning tasks.

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Area of Science:

  • Computer Science
  • Materials Science
  • Electrical Engineering

Background:

  • Traditional von Neumann architectures with separate processing and memory units face significant energy and time costs due to data movement.
  • The rapid expansion of data-intensive artificial intelligence applications exacerbates these limitations.
  • A paradigm shift towards non-von Neumann architectures is necessary to address these challenges.

Purpose of the Study:

  • To provide a comprehensive overview of in-memory computing as a non-von Neumann approach.
  • To explore computational primitives enabled by memory devices for in-memory computing.
  • To highlight the diverse applications of in-memory computing in scientific computing, AI, and beyond.

Main Methods:

  • Reviewing charge-based and resistance-based memory devices for in-memory computing.
  • Analyzing the physical attributes of memory devices to perform computations in situ.
  • Surveying existing and potential applications across various computational domains.

Main Results:

  • In-memory computing leverages physical properties of memory devices for in-place computation.
  • Key computational primitives are enabled by both charge-based and resistance-based memory technologies.
  • Successful demonstrations span scientific computing, signal processing, optimization, machine learning, deep learning, and stochastic computing.

Conclusions:

  • In-memory computing presents a viable solution to the von Neumann bottleneck, particularly for data-centric AI workloads.
  • Exploiting memory device physics offers a pathway to more efficient and powerful computing systems.
  • This approach holds significant promise for advancing fields reliant on large-scale data processing and complex computations.