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  • 1.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Senior, Alexander
    Monash University, Melbourne, VIC.
    Khan, Asad
    Monash University, Melbourne, VIC.
    Sekercioglu, Ahmet
    Monash University, Melbourne, VIC.
    Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing2017In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 28, no 6, p. 1250-1262Article in journal (Refereed)
    Abstract [en]

    This article proposes the use of Vector Symbolic Architectures for implementing Hierarchical Graph Neuron, an architecture for memorizing patterns of generic sensor stimuli. The adoption of a Vector Symbolic representation ensures a one-layered design for the approach, while maintaining the previously reported properties and performance characteristics of Hierarchical Graph Neuron, and also improving the noise resistance of the architecture. The proposed architecture enables a linear (with respect to the number of stored entries) time search for an arbitrary sub-pattern.

  • 2.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Rahimi, Abbas
    University of California at Berkeley, Berkeley.
    Rachkovskij, Dmitri A.
    International Research and Training, Center for Information Technologies and Systems, Kiev, Ukraine.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Rabaey, Jan M.
    University of California at Berkeley, Berkeley.
    Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics2018In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 29, no 12, p. 5880-5898Article in journal (Refereed)
    Abstract [en]

    Hyperdimensional (HD) computing is a promising paradigm for future intelligent electronic appliances operating at low power. This paper discusses tradeoffs of selecting parameters of binary HD representations when applied to pattern recognition tasks. Particular design choices include density of representations and strategies for mapping data from the original representation. It is demonstrated that for the considered pattern recognition tasks (using synthetic and real-world data) both sparse and dense representations behave nearly identically. This paper also discusses implementation peculiarities which may favor one type of representations over the other. Finally, the capacity of representations of various densities is discussed.

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