Spiking networks for improved cognitive abilities of edge computing devices

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

*Motsvarande författare för detta arbete

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

2 Citeringar (Scopus)

Sammanfattning

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.

OriginalspråkEngelska
Titel på värdpublikationPETRA 19: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments,
Antal sidor2
FörlagACM - Association for Computing Machinery
Utgivningsdatum05.06.2019
Sidor307-308
ISBN (elektroniskt)9781450362320
DOI
StatusPublicerad - 05.06.2019
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2019 - Rhodes, Grekland
Varaktighet: 05.06.201907.06.2019
Konferensnummer: 12
https://dl.acm.org/doi/proceedings/10.1145/3316782

Publikationsserier

NamnACM International Conference Proceeding Series

Nyckelord

  • 113 Data- och informationsvetenskap
  • 512 Företagsekonomi

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