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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.
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2017 (English)In: IEEE Transactions on Circuits and Systems Part 1: Regular Papers, ISSN 1549-8328, E-ISSN 1558-0806Article in journal (Refereed) Epub ahead of print
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.
Keyword [en]
Alternative computing, bio-inspired computing, hyperdimensional computing, vector symbolic architectures, in-memory computing
National Category
Computer Systems
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-63783DOI: 10.1109/TCSI.2017.2705051OAI: oai:DiVA.org:ltu-63783DiVA: diva2:1106675
Available from: 2017-06-08 Created: 2017-06-08 Last updated: 2017-06-19

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