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.
| Originalspråk | Engelska |
|---|---|
| Titel på värdpublikation | PETRA 19: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, |
| Antal sidor | 2 |
| Förlag | ACM - Association for Computing Machinery |
| Utgivningsdatum | 05.06.2019 |
| Sidor | 307-308 |
| ISBN (elektroniskt) | 9781450362320 |
| DOI | |
| Status | Publicerad - 05.06.2019 |
| MoE-publikationstyp | A4 Artikel i en konferenspublikation |
| Evenemang | ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2019 - Rhodes, Grekland Varaktighet: 05.06.2019 → 07.06.2019 Konferensnummer: 12 https://dl.acm.org/doi/proceedings/10.1145/3316782 |
Publikationsserier
| Namn | ACM International Conference Proceeding Series |
|---|
Nyckelord
- 113 Data- och informationsvetenskap
- 512 Företagsekonomi
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