Name
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.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3316782.3321546 | Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments |
Keywords | Field | DocType |
edge computing, interactive computation, spiking neural networks | Edge computing,Computer science,Human–computer interaction,Cognition,Interactive computation,Spiking neural network | Conference |
ISSN | ISBN | Citations |
Proceedings of the 12th ACM International Conference on PErvasive
Technologies Related to Assistive Environments (PETRA '19). ACM, New York,
NY, USA, 307-308. 2019 | 978-1-4503-6232-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anton Akusok | 1 | 143 | 10.72 |
| Kaj-Mikael Björk | 2 | 148 | 16.40 |
| Leonardo Espinosa Leal | 3 | 1 | 1.02 |
| Yoan Miche | 4 | 1054 | 54.56 |
| Renjie Hu | 5 | 0 | 0.34 |
| Amaury Lendasse | 6 | 1876 | 126.03 |