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An On-Demand Neuromorphic Vision System Enabled by a Multi-Paradigm Neuromorphic Device and Hierarchical

Biyi Jiang1, Jiayi Xu1, Liang Ran2

  • 1School of Microelectronics, Southern University of Science and Technology, Shenzhen, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
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This study introduces an ultra-reconfigurable intelligent vision system, bridging the gap between flexibility and power efficiency. The novel system achieves superior performance for dynamic and static scenarios, outperforming current technologies.

Keywords:
general purposeneuromorphic devicevision system

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Area of Science:

  • Neuromorphic Engineering
  • Computer Vision
  • Materials Science

Background:

  • Current intelligent vision systems lack the flexibility and power efficiency needed for dynamic scenarios.
  • Existing systems rely on rigid architectures and limited-function devices, hindering adaptability.
  • Biological vision systems offer a benchmark for high intelligence, efficiency, and flexibility.

Purpose of the Study:

  • To develop an on-demand ultra-reconfigurable intelligent vision system.
  • To bridge the neuromorphic gap by enabling seamless switching between functionalities.
  • To achieve concurrent high intelligence, efficiency, and flexibility in vision systems.

Main Methods:

  • Developed a multi-paradigm device array capable of switching between spiking, non-spiking, neuromorphic imaging (NI), and artificial intelligence (AI) modes.
  • Implemented a reconfigurable circuit and architecture design for on-demand resource allocation.
  • Demonstrated true reconfigurability at device, cell, array, and system levels.

Main Results:

  • Achieved superior power efficiencies of up to 52.6 TOPS/W (NI-centric) and 75.5 TOPS/W (NI/AI hybrid).
  • Demonstrated on-demand resource allocation between NI and AI functionalities for smart imaging and recognition.
  • Enabled seamless transitions between spiking (dynamic) and non-spiking (static) modes.

Conclusions:

  • The developed system offers unprecedented reconfigurability and power efficiency for intelligent vision.
  • This work represents a significant advancement towards mimicking biological vision system capabilities.
  • The system provides a flexible and efficient solution for dynamic and unpredictable visual processing tasks.