A Survey on Hyperdimensional Computing aka Vector Symbolic
Architectures, Part I: Models and Data Transformations
- URL: http://arxiv.org/abs/2111.06077v2
- Date: Mon, 31 Jul 2023 22:40:51 GMT
- Title: A Survey on Hyperdimensional Computing aka Vector Symbolic
Architectures, Part I: Models and Data Transformations
- Authors: Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov, Abbas Rahimi
- Abstract summary: HDC/VSA is a highly interdisciplinary field with connections to computer science, electrical engineering, artificial intelligence, mathematics, and cognitive science.
This two-part comprehensive survey is written to be useful for both newcomers and practitioners.
- Score: 7.240104756698618
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 vector distributed 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 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.
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