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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Engineering

    Background:

    • Memory-augmented neural networks (MANNs) utilize external key-value (KV) memory.
    • The complexity of traditional KV memory is often limited by the number of support vectors.
    • This poses challenges for efficient implementation, particularly on specialized hardware.

    Purpose of the Study:

    • To propose a generalized KV memory architecture.
    • To decouple the memory dimension from the number of support vectors.
    • To enable flexible control over the robustness-resource tradeoff for MANNs.

    Main Methods:

    • Introduced a free parameter to control redundancy in the key memory representation.
    • Developed a generalized KV memory that decouples dimension from support vectors.
    • Exploited nonvolatile memory devices for dense storage and computation in in-memory computing hardware.

    Main Results:

    • The generalized KV memory offers an additional degree of freedom for optimization.
    • Effectively mitigated up to 44% of nonidealities in in-memory computing hardware.
    • Achieved mitigation without compromising accuracy or the number of devices, and without retraining the neural network.

    Conclusions:

    • The proposed generalized KV memory enhances MANNs by offering a flexible and efficient memory solution.
    • This approach is particularly beneficial for resource-constrained environments and specialized hardware like in-memory computing systems.
    • The ability to adapt memory representation on demand significantly improves hardware nonideality mitigation.