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    Area of Science:

    • Neuromorphic engineering
    • Materials science

    Background:

    • Oscillatory neural networks (ONNs) leverage coupled oscillators for analog computation.
    • Vanadium dioxide (VO2) oscillators are promising for low-power, edge AI applications like pattern recognition.
    • Limited understanding exists regarding ONN hardware scalability and performance.

    Purpose of the Study:

    • To evaluate the performance, scalability, and energy efficiency of VO2-based ONNs at the circuit and architecture levels.
    • To investigate how ONN computation time, energy consumption, and memory capacity scale with network size.
    • To identify design parameters for minimizing ONN energy consumption.

    Main Methods:

    • Circuit-level simulations of VO2-oscillator based ONNs.
    • Technology computer-aided design (TCAD) simulations to optimize VO2 device dimensions in crossbar (CB) geometry.
    • Benchmarking ONN performance against state-of-the-art architectures.
    • Evaluating ONN for image edge detection and comparing with Sobel and Canny methods.

    Main Results:

    • ONN energy scales linearly with the number of oscillators, indicating suitability for large-scale edge integration.
    • Scaling down VO2 device dimensions in CB geometry reduces oscillator voltage and energy consumption.
    • ONNs demonstrate competitive energy efficiency for devices oscillating above 100 MHz.
    • ONNs effectively perform image edge detection, comparable to traditional methods.

    Conclusions:

    • VO2-based ONNs represent a scalable and energy-efficient neuromorphic computing paradigm for edge AI.
    • Optimized VO2 device design and ONN architecture enable high performance and low power consumption.
    • ONNs offer a viable alternative for resource-constrained edge devices, particularly for tasks like image processing.