Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
High-Dimensional Computing as a Nanoscalable Paradigm
Department of Electrical Engineering and Computer Sciences, University of California at Berkeley.
Department of Electrical Engineering and Computer Sciences, University of California at Berkeley .
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Helen Wills Neuroscience Institute, University of California at Berkeley.
Show others and affiliations
2017 (English)In: IEEE Transactions on Circuits and Systems Part 1: Regular Papers, ISSN 1549-8328, E-ISSN 1558-0806, Vol. 64, no 9, 2508-2521 p.Article in journal (Refereed) Published
Abstract [en]

We outline a model of computing with high-dimensional (HD) vectors—where the dimensionality is in the thousands. It is built on ideas from traditional (symbolic) computing and artificial neural nets/deep learning, and complements them with ideas from probability theory, statistics, and abstract algebra. Key properties of HD computing include a well-defined set of arithmetic operations on vectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operation, making it possible to develop efficient algorithms for large-scale real-world tasks. We present a 2-D architecture and demonstrate its functionality with examples from text analysis, pattern recognition, and biosignal processing, while achieving high levels of classification accuracy (close to or above conventional machine-learning methods), energy efficiency, and robustness with simple algorithms that learn fast. HD computing is ideally suited for 3-D nanometer circuit technology, vastly increasing circuit density and energy efficiency, and paving a way to systems capable of advanced cognitive tasks.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 64, no 9, 2508-2521 p.
Keyword [en]
Alternative computing, bio-inspired computing, hyperdimensional computing, vector symbolic architectures, in-memory computing
National Category
Computer Systems Computer Science
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-63783DOI: 10.1109/TCSI.2017.2705051ISI: 000409058000026OAI: oai:DiVA.org:ltu-63783DiVA: diva2:1106675
Note

Validerad;2017;Nivå 2;2017-08-31 (rokbeg)

Available from: 2017-06-08 Created: 2017-06-08 Last updated: 2017-11-24Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full texthttp://ieeexplore.ieee.org/document/7942066/

Authority records BETA

Kleyko, Denis

Search in DiVA

By author/editor
Kleyko, Denis
By organisation
Computer Science
In the same journal
IEEE Transactions on Circuits and Systems Part 1: Regular Papers
Computer SystemsComputer Science

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 136 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf