Spiking networks for improved cognitive abilities of edge computing devices

Anton Akusok*, Kaj Mikael Björk, Leonardo Espinosa Leal, Yoan Miche, Renjie Hu, Amaury Lendasse

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices. Such approach is a response to the arising need of processing data generated by natural person (a human being), also known as personal data. Spiking Neural networks are the core method behind it: suitable for a low latency energy-constrained hardware, enabling local training or re-training, while not taking advantage of scalability available in the Cloud.

Original languageEnglish
Title of host publicationPETRA 19: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments,
Number of pages2
PublisherACM - Association for Computing Machinery
Publication date05.06.2019
Pages307-308
ISBN (Electronic)9781450362320
DOIs
Publication statusPublished - 05.06.2019
MoE publication typeA4 Article in conference proceedings
EventACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2019 - Rhodes, Greece
Duration: 05.06.201907.06.2019
Conference number: 12
https://dl.acm.org/doi/proceedings/10.1145/3316782

Publication series

NameACM International Conference Proceeding Series

Keywords

  • 113 Computer and information sciences
  • Edge computing
  • Interactive computation
  • Spiking neural networks
  • 512 Business and Management

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  • Cite this

    Akusok, A., Björk, K. M., Leal, L. E., Miche, Y., Hu, R., & Lendasse, A. (2019). Spiking networks for improved cognitive abilities of edge computing devices. In PETRA 19: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, (pp. 307-308). (ACM International Conference Proceeding Series). ACM - Association for Computing Machinery. https://doi.org/10.1145/3316782.3321546