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A survey on hyperdimensional computing aka vector symbolic architectures: Part I: Models and data transformations
University of California at Berkeley, California, USA; Research Institutes of Sweden, Kista, Sweden.ORCID iD: 0000-0002-6032-6155
International Research and Training Center for Information Technologies and Systems, Ukraine; Luleå University of Technology, Luleå, Sweden.ORCID iD: 0000-0002-3414-5334
Luleå University of Technology, Luleå, Sweden.ORCID iD: 0000-0003-0069-640X
IBM Research – Zurich, Zurich, Switzerland.ORCID iD: 0000-0003-3141-4970
2023 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 55, no 6, article id 130Article in journal (Refereed) Published
Abstract [en]

This two-part comprehensive survey is 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 distributed vector representations. Notable models in the HDC/VSA family are Tensor Product Representations, Holographic Reduced Representations, Multiply-Add-Permute, Binary Spatter Codes, and Sparse Binary Distributed Representations but there are other models too. HDC/VSA is a highly interdisciplinary field with connections to computer science, electrical engineering, artificial intelligence, mathematics, and cognitive science. This fact makes it challenging to create a thorough overview of the field. However, due to a surge of new researchers joining the field in recent years, the necessity for a comprehensive survey of the field has become extremely important. Therefore, amongst other aspects of the field, this Part I surveys important aspects such as: known computational models of HDC/VSA and transformations of various input data types to high-dimensional distributed representations. Part II of this survey [84] is devoted to applications, cognitive computing and architectures, as well as directions for future work. The survey is written to be useful for both newcomers and practitioners

Place, publisher, year, edition, pages
New York: ACM Publications, 2023. Vol. 55, no 6, article id 130
Keywords [en]
Artificial intelligence, machine learning, distributed representations, data structures, hyperdimensional computing, vector symbolic architectures, holographic reduced representations, tensor product representations, matrix binding of additive terms, binary spatter codes, multiply-add-permute, sparse binary distributed representations, sparse block codes, modular composite representations, geometric analogue of holographic reduced representations
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-116237DOI: 10.1145/3538531ISI: 000893245700022Scopus ID: 2-s2.0-85146491559OAI: oai:DiVA.org:oru-116237DiVA, id: diva2:1900336
Funder
EU, Horizon 2020, 839179Swedish Foundation for Strategic Research, UKR22-0024
Note

The work of DK was supported by the European Union's Horizon 2020 Programme under the Marie Sklodowska-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, 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: 2024-09-23 Created: 2024-09-23 Last updated: 2024-09-23Bibliographically approved

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Kleyko, Denis

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