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Vector Symbolic Architectures as a Computing Framework for Emerging Hardware.

Denis Kleyko1, Mike Davies2, E Paxon Frady2

  • 1Redwood Center for Theoretical Neuroscience at the University of California at Berkeley, CA 94720, USA and also with the Intelligent Systems Lab at Research Institutes of Sweden, 16440 Kista, Sweden.

Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
|October 23, 2023
PubMed
Summary
This summary is machine-generated.

Vector Symbolic Architectures (VSA), also known as Hyperdimensional Computing, offer powerful operations on high-dimensional vectors suitable for AI and emerging hardware. This computing framework excels at complex problems and distributed representations for advanced computing applications.

Keywords:
Turing completenesscomputing frameworkcomputing in superpositiondata structuresdistributed representationsemerging hardwarehyperdimensional computingvector symbolic architectures

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Emerging hardware necessitates novel computing frameworks.
  • Artificial Intelligence (AI) requires efficient cognitive operation processing.
  • Conventional computing faces challenges with complex combinatorial search problems.

Purpose of the Study:

  • To review recent advancements in Vector Symbolic Architectures (VSA).
  • To highlight VSA's suitability for AI and stochastic, emerging hardware.
  • To demonstrate VSA's potential as a computational framework and abstraction layer.

Main Methods:

  • Review of Vector Symbolic Architectures (VSA) principles and operations.
  • Analysis of VSA's algebraic structure for high-dimensional vector manipulation.
  • Illustration of VSA's "computing in superposition" feature.

Main Results:

  • VSA provides simple, powerful operations on high-dimensional vectors supporting modern computing needs.
  • VSA's "computing in superposition" enables efficient solutions for AI's combinatorial search problems.
  • VSA demonstrates potential for computational universality and distributed representations.

Conclusions:

  • Vector Symbolic Architectures (VSA) are a promising framework for AI and emerging hardware.
  • VSA offers a unique approach to computing with distributed representations.
  • VSA can serve as an abstraction layer for novel computing architectures like neuromorphic computing.