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    This study presents a neuromorphic system using complementary metal-oxide-semiconductor (CMOS) hardware and synaptic time-multiplexing (STM) to overcome scalability and connectivity challenges in artificial neural networks. A novel programmable front-end with a neuromorphic compiler enables automatic configuration for diverse neural architectures.

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    Area of Science:

    • Neuromorphic Engineering
    • Computer Architecture
    • Artificial Intelligence Hardware

    Background:

    • Designing neuromorphic hardware to mimic biological systems faces significant scalability and connectivity challenges.
    • Existing approaches often struggle to achieve the density and flexibility required for complex neural architectures.
    • Traditional complementary metal-oxide-semiconductor (CMOS) technology offers a viable platform but requires innovative design strategies.

    Purpose of the Study:

    • To propose a novel neuromorphic system architecture design that addresses scalability and connectivity using traditional CMOS hardware.
    • To detail a programmable front-end capable of automatically configuring hardware to emulate diverse neural architectures.
    • To introduce synaptic time-multiplexing (STM) as a core concept for achieving high connectivity and scalability.

    Main Methods:

    • Development of a neuromorphic compiler to translate neural architectures into hardware switch configurations.
    • Integration of a digital memory to store these configurations, enabling dynamic emulation of neural models.
    • Implementation of synaptic time-multiplexing (STM) to manage synaptic states efficiently within the CMOS architecture.

    Main Results:

    • The proposed system architecture effectively addresses key challenges in scalability and connectivity for neuromorphic hardware.
    • The programmable front-end, incorporating the neuromorphic compiler and digital memory, allows for automatic and flexible configuration of neural architectures.
    • Synaptic time-multiplexing (STM) demonstrates its capability to enhance the density and efficiency of synaptic connections in CMOS-based neuromorphic systems.

    Conclusions:

    • The described neuromorphic system design, leveraging CMOS hardware and STM, offers a practical approach to building scalable and highly connected artificial neural networks.
    • The programmable front-end represents a significant advancement in the automatic configuration and emulation of complex neural architectures.
    • This work paves the way for future extensions and applications in advanced neuromorphic computing.