Vector symbolic architectures as a computing framework for emerging hardwareShow others and affiliations
2022 (English)In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 110, no 10, p. 1538-1571Article in journal (Refereed) Published
Abstract [en]
This article reviews recent progress in the development of the computing framework vector symbolic architectures (VSA) (also known as hyperdimensional computing). This framework is well suited for implementation in stochastic, emerging hardware, and it naturally expresses the types of cognitive operations required for artificial intelligence (AI). We demonstrate in this article that the field-like algebraic structure of VSA offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of VSA, “computing in superposition,” which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that VSA are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind VSA, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing.
Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2022. Vol. 110, no 10, p. 1538-1571
Keywords [en]
Computing framework, computing in superposition, data structures, distributed representations, emerging hardware, holographic reduced representation (HRR), hyperdimensional (HD) computing, Turing completeness, vector symbolic architectures (VSA)
National Category
Computer Sciences Computer Systems
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:oru:diva-116040DOI: 10.1109/JPROC.2022.3209104ISI: 000870302900008Scopus ID: 2-s2.0-85141794287OAI: oai:DiVA.org:oru-116040DiVA, id: diva2:1898462
Projects
Defense Advanced Research Projects Agency’s (DARPA)VIP (Super-HD Project)AIE (HyDDENN Project)Intel’s THWAI
Funder
EU, Horizon 2020, 839179Swedish Foundation for Strategic Research, UKR22-0024
Note
The work of Denis Kleyko was supported in part by the European Union's Horizon 2020 Research and Innovation Programme under Marie Sklodowska-Curie Grant Agreement 839179, in part by the Defense Advanced Research Projects Agency's (DARPA) VIP (Super-HD Project) and AIE (HyDDENN Project) Programs, in part by the Air Force Office of Scientific Research (AFOSR) under Grant FA9550-19-1-0241, and in part by the Intel's THWAI Program. The work of Pentti Kanerva was supported in part by the DARPA's VIP (Super-HD Project) and AIE (HyDDENN Project) Programs and in part by AFOSR under Grant FA9550-19-1-0241. The work of Bruno A. Olshausen was supported in part by the DARPA's VIP (Super-HD Project) and AIE (HyDDENN Project) Programs, in part by AFOSR under Grant FA9550-19-1-0241, and in part by the Intel's THWAI Program. The work of Jan M. Rabaey was supported in part by the DARPA's VIP (Super-HD Project) and AIE (HyDDENN Project) Programs. The work of Dmitri A. Rachkovskij was supported in part by the National Academy of Sciences of Ukraine under Grant 0120U000122, Grant 0121U000016, Grant 0122U002151, and Grant 0117U002286; in part by the Ministry of Education and Science of Ukraine under Grant 0121U000228 and Grant 0122U000818; and in part by the Swedish Foundation for Strategic Research (SSF) under Grant UKR22-0024. The work of Friedrich T. Sommer was supported in part by the Intel's THWAI Program, in part by NIH under Grant R01-EB026955, and in part by NSF under Grant IIS-1718991.
2024-09-172024-09-172024-09-18Bibliographically approved