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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Natural Language Processing

    Background:

    • Memory-augmented neural networks (MANN) are powerful tools for complex reasoning tasks.
    • However, MANNs often require a high number of attention inference hops, leading to significant computational overhead.
    • Efficiently managing these computational resources is crucial for practical applications.

    Purpose of the Study:

    • To propose an online adaptive approach, termed [Formula: see text]-memory-augmented neural network ([Formula: see text]-MANN), to reduce attention inference hops in MANNs.
    • To introduce weight pruning techniques for the fully connected layers of [Formula: see text]-MANN to further decrease computational costs.
    • To evaluate the effectiveness of the proposed methods on question answering (QA) datasets and MANN architectures.

    Main Methods:

    • An online adaptive strategy using a small neural network classifier to determine the optimal number of attention inference hops for each input query.
    • Development of two weight pruning approaches for the final fully connected layers: one with negligible accuracy loss and another with controllable accuracy trade-offs.
    • Application and assessment of the [Formula: see text]-MANN approach on two distinct MANN structures and two QA datasets.

    Main Results:

    • Achieved an average reduction of 50% in computations compared to baseline MANNs, with less than 1% accuracy loss.
    • Combined with the zero-skipping technique, the approach reduced computation counts by approximately 70%.
    • Demonstrated an average runtime reduction of 43% on CPU and GPU platforms.

    Conclusions:

    • The proposed [Formula: see text]-MANN approach effectively reduces computational complexity in memory-augmented neural networks.
    • Weight pruning further enhances efficiency with minimal impact on accuracy.
    • The method offers significant performance improvements for QA systems and other applications leveraging MANNs.