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This study introduces a novel Mem-Selector device for edge vision systems, enabling in-memory pruning-computing (IMPC) that reduces energy consumption and improves robustness for AI hardware.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Conventional edge vision systems suffer from high power consumption and latency due to separate pruning, memory, and processing units.
  • Human visual selective attention offers a model for efficient information processing in edge devices.

Purpose of the Study:

  • To develop a multifunctional device for in-memory pruning-computing (IMPC) inspired by human visual attention.
  • To investigate the switching mechanisms within a novel Mem-Selector (M-S) device.

Main Methods:

  • Fabrication and characterization of a Ta/TaOx/Ta2O5 Mem-Selector device.
  • Utilizing transmission electron microscopy (TEM) to observe filament formation and nanocrystalline cluster growth.
  • Construction and evaluation of an IMPC system for adaptive information pruning and processing.

Main Results:

  • The M-S device exhibits both resistive memory and threshold switching characteristics, indicating coexisting ionic and electronic mechanisms.
  • The IMPC system adaptively prunes trivial information, optimizing hardware cost and classification performance.
  • Significant reductions in input energy consumption (29-90%) with minimal accuracy loss (<1%) were achieved.

Conclusions:

  • The developed M-S device and IMPC system offer a promising solution for energy-efficient, high-performance edge hardware.
  • Hardware-software co-design is crucial for advancing edge computing capabilities.
  • The study highlights the potential of bio-inspired computing for next-generation AI systems.