A semi-holographic hyperdimensional representation system for hardware-friendly cognitive computing.

hardware hyperdimensional processor

Journal

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
ISSN: 1471-2962
Titre abrégé: Philos Trans A Math Phys Eng Sci
Pays: England
ID NLM: 101133385

Informations de publication

Date de publication:
07 Feb 2020
Historique:
entrez: 24 12 2019
pubmed: 24 12 2019
medline: 24 12 2019
Statut: ppublish

Résumé

One of the main, long-term objectives of artificial intelligence is the creation of thinking machines. To that end, substantial effort has been placed into designing cognitive systems; i.e. systems that can manipulate semantic-level information. A substantial part of that effort is oriented towards designing the mathematical machinery underlying cognition in a way that is very efficiently implementable in hardware. In this work, we propose a 'semi-holographic' representation system that can be implemented in hardware using only multiplexing and addition operations, thus avoiding the need for expensive multiplication. The resulting architecture can be readily constructed by recycling standard microprocessor elements and is capable of performing two key mathematical operations frequently used in cognition, superposition and binding, within a budget of below 6 pJ for 64-bit operands. Our proposed 'cognitive processing unit' is intended as just one (albeit crucial) part of much larger cognitive systems where artificial neural networks of all kinds and associative memories work in concord to give rise to intelligence. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

Identifiants

pubmed: 31865886
doi: 10.1098/rsta.2019.0162
pmc: PMC6939245
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

20190162

Références

Biol Cybern. 1987;56(5-6):367-74
pubmed: 3620535
Neural Comput. 1991 Summer;3(2):246-257
pubmed: 31167308
Nature. 2015 May 28;521(7553):436-44
pubmed: 26017442
IEEE Trans Neural Netw. 1995;6(3):623-41
pubmed: 18263348
Appl Opt. 1989 Jan 15;28(2):272-83
pubmed: 20548469
Nature. 1969 Jun 7;222(5197):960-2
pubmed: 5789326
Front Neurosci. 2015 Apr 29;9:141
pubmed: 25972778
Appl Opt. 1987 Dec 1;26(23):5039-54
pubmed: 20523483
IEEE Trans Neural Netw Learn Syst. 2017 Oct;28(10):2222-2232
pubmed: 27411231
Front Psychol. 2017 Sep 12;8:1551
pubmed: 28955272

Auteurs

A Serb (A)

Zepler Institute, University of Southampton, Southampton SO17 1BJ, UK.

I Kobyzev (I)

David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada N2L 3G1.

J Wang (J)

Zepler Institute, University of Southampton, Southampton SO17 1BJ, UK.

T Prodromakis (T)

Zepler Institute, University of Southampton, Southampton SO17 1BJ, UK.

Classifications MeSH