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    Neuromorphic CMOS-memristive architectures offer a low-power solution for edge computing devices, addressing challenges in cloud infrastructure. This review explores their benefits and drawbacks for efficient data processing.

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    Area of Science:

    • Computer Engineering
    • Materials Science
    • Artificial Intelligence

    Background:

    • The proliferation of Internet of Things (IoT) sensors generates massive data, straining cloud computing resources in terms of power, scalability, and sustainability.
    • Edge computing offers a solution by processing data closer to the source, reducing cloud overheads.
    • Developing low-power, high-performance edge devices is crucial for efficient data handling.

    Purpose of the Study:

    • To review neuromorphic CMOS-memristive architectures for integration into edge computing devices.
    • To elucidate the advantages of neuromorphic computing for edge applications.
    • To identify the drawbacks and open challenges in neuromemristive circuits for edge computing.

    Main Methods:

    • Literature review of existing neuromorphic CMOS-memristive architectures.
    • Analysis of power consumption and processing capabilities of these architectures.
    • Comparative study of their suitability for edge computing environments.

    Main Results:

    • Neuromorphic architectures, particularly those utilizing memristive devices, show significant potential for low-power, high-efficiency data processing at the edge.
    • These architectures can mimic brain-like functions, enabling complex computations on edge devices.
    • Key challenges include device variability, fabrication complexity, and integration with existing CMOS technology.

    Conclusions:

    • Neuromorphic CMOS-memristive circuits are promising for enhancing edge computing capabilities.
    • Further research is needed to overcome fabrication and integration challenges for widespread adoption.
    • These advancements are vital for sustainable and scalable edge AI solutions.