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Evgeny Osipov, Sachin Kahawala, Dilantha Haputhanthri

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    Hyperseed, a new unsupervised machine learning algorithm, enables fast learning on neuromorphic hardware using vector symbolic architectures (VSAs). It excels in few-shot learning and topology preservation for unlabeled data.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Neuromorphic Engineering

    Background:

    • Recent advancements in biologically inspired neuromorphic hardware.
    • Need for efficient unsupervised machine learning algorithms for unlabeled data.

    Purpose of the Study:

    • Introduce Hyperseed, a novel unsupervised machine learning algorithm.
    • Leverage Vector Symbolic Architectures (VSAs) for fast topology-preserving feature mapping.
    • Adapt the algorithm for implementation on spiking neuromorphic hardware.

    Main Methods:

    • Developed Hyperseed algorithm based on VSA principles (binding and bundling).
    • Utilized Fourier holographic reduced representations (FHRR) model for neuromorphic hardware suitability.
    • Employed few-shot learning and a single vector operation learning rule.

    Main Results:

    • Empirically evaluated Hyperseed on synthetic datasets.
    • Demonstrated effectiveness on benchmark tasks: IRIS classification and language identification (n-gram statistics).
    • Confirmed Hyperseed's capability for fast learning and topology preservation.

    Conclusions:

    • Hyperseed offers efficient unsupervised learning for neuromorphic hardware.
    • The algorithm's few-shot learning and single vector operation are key contributions.
    • Validated applications in classification and language identification tasks.