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    Emerging memory technology (EMT) offers efficient in-memory deep learning but suffers from instability. We developed optimization techniques to overcome EMT

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Hardware Engineering

    Background:

    • In-memory deep learning enhances efficiency by processing data where it's stored, reducing energy and time costs.
    • Emerging memory technology (EMT) promises further gains in density, energy, and performance for in-memory computing.
    • A key challenge with EMT is its intrinsic instability, leading to data read fluctuations and potential accuracy loss in deep learning models.

    Purpose of the Study:

    • To address the instability of emerging memory technology (EMT) in in-memory deep learning.
    • To develop mathematical optimization techniques that mitigate accuracy loss caused by EMT fluctuations.
    • To enhance both the accuracy and energy efficiency of in-memory deep learning models utilizing EMT.

    Main Methods:

    • Proposed three novel mathematical optimization techniques specifically designed to counteract EMT instability.
    • Integrated these techniques into in-memory deep learning frameworks.
    • Conducted experiments to evaluate the impact on model accuracy and energy efficiency.

    Main Results:

    • The proposed optimization techniques successfully overcame the instability issues inherent in EMT.
    • Achieved full recovery of state-of-the-art (SOTA) accuracy for most tested deep learning models.
    • Demonstrated at least an order of magnitude improvement in energy efficiency compared to existing SOTA methods.

    Conclusions:

    • Mathematical optimization can effectively resolve the accuracy degradation caused by unstable emerging memory technology.
    • The developed techniques enable the realization of highly accurate and energy-efficient in-memory deep learning systems.
    • This work paves the way for more robust and performant applications of EMT in AI hardware.