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Oscillatory Neural Network with High-Order Sub-Harmonic Injection Locking.

Ye-Seong Chung1, Seong-Yun Yun1, Sang-Won Lee1

  • 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

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Summary
This summary is machine-generated.

Researchers demonstrated high-order harmonic injection locking in oscillatory neural networks (ONNs) using biristors. This advancement enables multistate phase quantization for solving complex graph coloring problems in neuromorphic computing.

Keywords:
biristorcoupled oscillatorsgraph coloringhigh-order sub-harmonic injection lockingoscillatory neural network (ONN)phase quantization

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Area of Science:

  • Neuromorphic Engineering
  • Nonlinear Dynamics
  • Materials Science

Background:

  • Oscillatory neural networks (ONNs) leverage oscillator phase for computation.
  • Sub-harmonic injection locking (SHIL) is established for N=1, 2 harmonics in ONNs.
  • High-order Nth-harmonic injection locking (N >= 3) in ONNs remains experimentally undemonstrated.

Purpose of the Study:

  • To experimentally demonstrate high-order Nth-harmonic injection locking (Nth-HIL) in ONNs.
  • To utilize multistate phase quantization for solving graph coloring problems.
  • To advance the computational capacity of phase-based neuromorphic hardware.

Main Methods:

  • Utilized a submicrometer-scale bistable resistor (biristor) as the oscillator.
  • Applied external signals at N times the biristor's natural frequency to induce Nth-HIL.
  • Implemented ONNs leveraging the quantized phase states for graph coloring.

Main Results:

  • Successfully demonstrated Nth-HIL for N=1 to 9 using biristor oscillators.
  • Observed output phase quantization into N discrete states upon Nth-harmonic injection.
  • Solved graph coloring problems and determined chromatic numbers using the developed ONNs.

Conclusions:

  • Experimental validation of high-order Nth-HIL in ONNs using biristors.
  • Multistate phase quantization offers a novel computational approach for graph problems.
  • Establishes a foundation for enhanced phase-based neuromorphic computing capabilities.