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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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In-Sensor-Memory Computing for Post-Von Neumann Intelligence: A Perspective.

Hongyu Tang1,2, Ninghai Yu3, Pengsheng Min3

  • 1College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, People's Republic of China. hongyu_tang@fudan.edu.cn.

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Summary
This summary is machine-generated.

In-sensor-memory computing (ISMC) overcomes limitations of traditional architectures by integrating processing and memory. This approach enables energy-efficient, distributed artificial intelligence at the data source.

Keywords:
In-sensor-memory computing (ISMC)Industry–academia–research (IAR)Neuromorphic hardwarePost-von Neumann intelligence

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Area of Science:

  • Computer Science
  • Materials Science
  • Electrical Engineering

Background:

  • Conventional von Neumann architectures face limitations due to data movement between sensing, memory, and computation.
  • The slowing of transistor scaling necessitates architectural innovation for advancing intelligent systems.

Purpose of the Study:

  • To survey technological foundations, architectural trends, and applications of In-Sensor-Memory Computing (ISMC).
  • To examine global collaborations and identify challenges in ISMC development.

Main Methods:

  • Co-locating perception, storage, and computation within unified device and system architectures.
  • Leveraging advances in memristive/ferroelectric devices, novel materials, 3D integration, and neuromorphic architectures.
  • Utilizing co-evolved algorithms like spiking neural networks and reservoir computing.

Main Results:

  • In-sensor-memory computing (ISMC) enables in situ signal processing, mixed-signal computation, and event-driven intelligence.
  • Recent technological and algorithmic advancements have expanded the capabilities of ISMC platforms.
  • ISMC offers a pathway to overcome the bottlenecks of traditional computing architectures.

Conclusions:

  • ISMC is a promising post-von Neumann hardware paradigm for energy-efficient, distributed intelligence.
  • Addressing challenges in variability, reliability, scalability, and benchmarking is crucial for ISMC's advancement.
  • ISMC is poised to revolutionize intelligent systems by enabling computation at the data source.