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Showing 1–5 of 5 results
Advanced filters: Author: Geethan Karunaratne Clear advanced filters

  • The implementation of memory-augmented neural networks using conventional computer architectures is challenging due to a large number of read and write operations. Here, Karunaratne, Schmuck et al. propose an architecture that enables analog in-memory computing on high-dimensional vectors at accuracy matching 32-bit software equivalent.

    • Geethan Karunaratne
    • Manuel Schmuck
    • Abbas Rahimi
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-12

  • Sensory signal attributes can be disentangled exploiting the computation-in-superposition capability of hyperdimensional computing, in-memory computing and associated intrinsic device-level stochasticity.

    • Jovin Langenegger
    • Geethan Karunaratne
    • Abbas Rahimi
    Research
    Nature Nanotechnology
    Volume: 18, P: 479-485

  • A complete in-memory hyperdimensional computing system, which uses 760,000 phase-change memory devices, can efficiently perform machine learning related tasks including language classification, news classification and hand gesture recognition from electromyography signals.

    • Geethan Karunaratne
    • Manuel Le Gallo
    • Abu Sebastian
    Research
    Nature Electronics
    Volume: 3, P: 327-337