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A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges
RISE Research Institutes of Sweden, Digital Systems, Data Science. University of California at Berkeley, USA; .ORCID iD: 0000-0002-6032-6155
International Research and Training Center for Information Technologies, Ukraine.
Luleå University of Technology, Sweden.
Ibm Research Zurich, Switzerland.
2023 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 55, no 9, article id 3558000Article in journal (Refereed) Published
Abstract [en]

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [321, 326] is an influential HDC/VSA model that is well known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field.Part I of this survey [222] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain; however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners. 

Place, publisher, year, edition, pages
Association for Computing Machinery , 2023. Vol. 55, no 9, article id 3558000
Keywords [en]
analogical reasoning, applications, Artificial intelligence, binary spatter codes, cognitive architectures, cognitive computing, distributed representations, geometric analogue of holographic reduced representations, holographic reduced representations, hyperdimensional computing, machine learning, matrix binding of additive terms, modular composite representations, multiply-add-permute, sparse binary distributed representations, sparse block codes, tensor product representations, vector symbolic architectures, Architecture, Codes (symbols), Computer architecture, Holography, Metadata, Binary spatter code, Composite representations, Distributed representation, Geometric analog of holographic reduced representation, Machine-learning, Matrix binding, Matrix binding of additive term, Modular composite representation, Modulars, Multiply-add, Product representation, Sparse binary distributed representation, Sparse block code, Tensor product representation, Tensor products, Vector symbolic architecture, Vectors
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-64721DOI: 10.1145/3558000Scopus ID: 2-s2.0-85147845869OAI: oai:DiVA.org:ri-64721DiVA, id: diva2:1757051
Note

Funding details: Air Force Office of Scientific Research, AFOSR, FA9550-19-1-0241; Funding details: Intel Corporation; Funding details: H2020 Marie Skłodowska-Curie Actions, MSCA, 839179; Funding details: Stiftelsen för Strategisk Forskning, SSF, UKR22-0024; Funding details: National Academy of Sciences of Ukraine, NASU, 0117U002286, 0120U000122, 0121U000016, 0122U002151; Funding details: Ministry of Education and Science of Ukraine, MESU, 0121U000228, 0122U000818; Funding text 1: The work of DK was supported by the European Union’s Horizon 2020 Programme under the Marie Skłodowska-Curie Individual Fellowship Grant (839179). The work of DK was also supported in part by AFOSR FA9550-19-1-0241 and Intel’s THWAI program. The work of DAR was supported in part by the National Academy of Sciences of Ukraine (grant no. 0120U000122, 0121U000016, 0122U002151, and 0117U002286), the Ministry of Education and Science of Ukraine (grant no. 0121U000228 and 0122U000818), and the Swedish Foundation for Strategic Research (SSF, grant no. UKR22-0024).; Funding text 2: The work of DK was supported by the European Union’s Horizon 2020 Programme under the Marie SkÅ odowska-Curie Individual Fellowship Grant (839179). The work of DK was also supported in part by AFOSR FA9550-19-1-0241 and Intel’s THWAI program. The work of DAR was supported in part by the National Academy of Sciences of Ukraine (grant no. 0120U000122, 0121U000016, 0122U002151, and 0117U002286), the Ministry of Education and Science of Ukraine (grant no. 0121U000228 and 0122U000818), and the Swedish Foundation for Strategic Research (SSF, grant no. UKR22-0024).

Available from: 2023-05-15 Created: 2023-05-15 Last updated: 2023-12-12Bibliographically approved

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