Journal article

Review of algorithms for artificial intelligence on low memory devices

Abstract:: The aim of the article is to conceptualise a more compact and efficient version of algorithms for artificial intelligence (AI). The core objective is to construct the design for a self-optimising and self-adapting autonomous artificial intelligence (AutoAI) that can be applied for edge analytics using real-time data. The methodology is based on synthesising existing knowledge on AI (i.e., knowledge modelling, symbolic reasoning, modal logic), with novel concepts from neuromorphic engineering in combination with deep learning algorithms (i.e., reinforcement learning, neural networks, evolutionary algorithms) and data science (i.e., statistics, linear regression, Bayesian methods). Far-reaching implications are expected from the unique integration of approaches in neuromorphic engineering and edge analytics.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Radanliev, P., & de Roure, D. (2021). Review of algorithms for artificial intelligence on low memory devices. IEEE Access, 9, 109986–109993.

MLA Style

Radanliev, P., and D. de Roure. “Review of Algorithms for Artificial Intelligence on Low Memory Devices.” IEEE Access, vol. 9, IEEE, 2021, pp. 109986–93.

Chicago Style

Radanliev, P, and D de Roure. 2021. “Review of Algorithms for Artificial Intelligence on Low Memory Devices.” IEEE Access 9: 109986–93.
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Files:: Radanliev_et_al_2021_Review_of_algorithms.pdf

(Preview, Version of record, 5.1MB, Terms of use)

Publisher copy:: 10.1109/ACCESS.2021.3101579

Authors

+ Radanliev, P More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author

+ de Roure, D More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author

+ Silicon Valley Community Foundation More from this funder

Grant:: 2019-205835 (3696)

+ Engineering and Physical Sciences Research Council More from this funder

Grant:: UCL REF 3641419

Publisher:: IEEE
Journal:: IEEE Access More from this journal
Volume:: 9
Pages:: 109986-109993
Publication date:: 2021-08-02
Acceptance date:: 2021-07-26
DOI:: 10.1109/ACCESS.2021.3101579
EISSN:: 2169-3536

Language:: English
Keywords:: low memory AI

artificial intelligence

algorithms

neuromorphic engineering

FFR

edge analytics

conceptual design
Pubs id:: 1187506
Local pid:: pubs:1187506
Deposit date:: 2021-07-26

Terms of use

Copyright holder:: Radanliev and de Roure
Copyright date:: 2021
Rights statement:: Copyright © The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Licence:: CC Attribution (CC BY)

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