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Sub-picojoule-per-bit volitional neuromorphic devices for precise targeting and tracking.

Yixuan Huang1, Qihao Sun1, Fuxing Dai2

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.

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This study introduces a novel neuromorphic device with active attention regulation, significantly improving energy efficiency for artificial intelligence hardware. This innovation enhances feature extraction and offers a sustainable path for hyperspectral imaging and computing.

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence Hardware
  • Energy-Efficient Computing

Background:

  • Artificial intelligence hardware faces energy consumption challenges.
  • Current retinomorphic devices have passive attention mechanisms limiting feature extraction flexibility.

Purpose of the Study:

  • To develop a volitional neuromorphic device with active attention regulation inspired by the human visual system.
  • To enhance feature extraction capabilities and energy efficiency in AI hardware.

Main Methods:

  • Leveraging gate-voltage-tunable photoconductance for adjustable differential spectral response.
  • Employing neural networks for spectral reconstruction accuracy evaluation.
  • Implementing active volitional attention regulation.

Main Results:

  • Achieved selective task perception through adjustable spectral response.
  • Demonstrated a data compression ratio of 1.17%.
  • Reached an information energy efficiency of 0.625 pJ/bit.

Conclusions:

  • The novel device advances retinomorphic hardware design.
  • Presents a sustainable pathway for energy-efficient hyperspectral imaging.
  • Enables next-generation neuromorphic computing systems.