Hyperdimensional computing: Difference between revisions

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{{Short description|Computational approach}}

'''Hyperdimensional computing''' ('''HDC''') is an approach to computation, particularly [[artificial general intelligence|Artificial General Intelligence]]. HDC is motivated by the observation that the [[Cerebellum|cerebellum cortex]] operates on high-dimensional data representations.<ref>{{Citation |last1=Zou |first1=Zhuowen |title=Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework |date=2021-10-01 |arxiv=2110.00214 |last2=Alimohamadi |first2=Haleh |last3=Imani |first3=Farhad |last4=Kim |first4=Yeseong |last5=Imani |first5=Mohsen}}</ref> In HDC, information is thereby represented as a hyperdimensional (long) [[Vector (mathematics and physics)|vector]] called hypervector ana array of numbershypervector. A hyperdimensional vector (hypervector) could include thousands of numbers that represent a point in a space of thousands of dimensions.,<ref name=":0">{{Cite web |last=Ananthaswamy |first=Anan |date=April 13, 2023 |title=A New Approach to Computation Reimagines Artificial Intelligence |url=https://www.quantamagazine.org/a-new-approach-to-computation-reimagines-artificial-intelligence-20230413/?mc_cid=ad9a93c472&mc_eid=506130a407 |website=Quanta Magazine}}</ref> Vectoras vector Symbolicsymbolic Architecturesarchitectures is an older name for the same broad approach.<ref name=":0"Research />extenuates for creating [[Artificial general intelligence|Artificial General Intelligence]].

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Hypervectors can also be used for reasoning. Raven's progressive matrices presents images of objects in a grid. One position in the grid is blank. The test is to choose from candidate images the one that best fits.<ref name=":0" />

A dictionary of hypervectors represents individual objects. Each hypervector represents an object concept with its attributes. For each test image a neural network generates a binary hypervector (valusvalues are +1 or −1) that is as close as possible to some set of dictionary hypervectors. The generated hypervector thus describes all the objects and their attributes in the image.<ref name=":0" />

Another algorithm creates probability distributions for the number of objects in each image and their characteristics. These probability distributions describe the likely characteristics of both the context and candidate images. They too are transformed into hypervectors, then algebra predicts the most likely candidate image to fill the slot.<ref name=":0" />

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== References ==

{{Reflist}}<references responsive="1"></references>

* {{Cite journal |last1=Kleyko |first1=Denis |last2=Rachkovskij |first2=Dmitri A. |last3=Osipov |first3=Evgeny |last4=Rahimi |first4=Abbas |date=2023-07-31 |title=A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations |url=https://dl.acm.org/doi/10.1145/3538531 |journal=ACM Computing Surveys |language=en |volume=55 |issue=6 |pages=1–40 |doi=10.1145/3538531 |issn=0360-0300|arxiv=2111.06077 }}

* {{Cite journal |last1=Kleyko |first1=Denis |last2=Rachkovskij |first2=Dmitri |last3=Osipov |first3=Evgeny |last4=Rahimi |first4=Abbas |date=2023-09-30 |title=A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges |url=https://dl.acm.org/doi/10.1145/3558000 |journal=ACM Computing Surveys |language=en |volume=55 |issue=9 |pages=1–52 |doi=10.1145/3558000 |issn=0360-0300|arxiv=2112.15424 }}

== External links ==

* {{Citation | vauthors=((Stock, M.)), ((Van Criekinge, W.)), ((Boeckaerts, D.)), ((Taelman, S.)), ((Van Haeverbeke, M.)), ((Dewulf, P.)), ((De Baets, B.)) | veditors=((Dutt, V.)) | year=2024 | title=Hyperdimensional computing: a fast, robust, and interpretable paradigm for biological data | publisher=Public Library of Science (PLOS) | journal = PLOS Computational Biology| volume=20 | issue=9 | pages=e1012426 | doi=10.1371/journal.pcbi.1012426 | doi-access=free | pmid=39316621 | arxiv=2402.17572 }}

* {{Cite journal |last=Kanerva |first=Pentti |date=2009-06-01 |title=Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors |url=https://doi.org/10.1007/s12559-009-9009-8 |journal=Cognitive Computation |language=en |volume=1 |issue=2 |pages=139–159 |doi=10.1007/s12559-009-9009-8 |s2cid=733980 |issn=1866-9964}}

* {{Citation | vauthors=((Cumbo, F.)), ((Chicco, D.)) | year=2025 | title=Hyperdimensional computing in biomedical sciences: a brief review| volume = 11 | issue = e2885 | journal = PeerJ Computer Science | pages=e2885 | doi=10.7717/peerj-cs.2885 | doi-access=free | pmc=12192801 }}

* {{Cite journal |last=Kanerva |first=Pentti |date=2009-06-01 |title=Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors |url=https://doi.org/10.1007/s12559-009-9009-8 |journal=Cognitive Computation |language=en |volume=1 |issue=2 |pages=139–159 |doi=10.1007/s12559-009-9009-8 |s2cid=733980 |issn=1866-9964|url-access=subscription }}

* {{Cite journal |last1=Neubert |first1=Peer |last2=Schubert |first2=Stefan |last3=Protzel |first3=Peter |date=2019-12-01 |title=An Introduction to Hyperdimensional Computing for Robotics |url=https://doi.org/10.1007/s13218-019-00623-z |journal=KI – Künstliche Intelligenz |language=en |volume=33 |issue=4 |pages=319–330 |doi=10.1007/s13218-019-00623-z |s2cid=202642163 |issn=1610-1987|url-access=subscription }}

* {{Cite arXiv |last1=Neubert |first1=Peer |last2=Schubert |first2=Stefan |date=2021-01-19 |title=Hyperdimensional computing as a framework for systematic aggregation of image descriptors |class=cs.CV |eprint=2101.07720v1 |language=en}}