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 language | English |
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| Title of host publication | PETRA 19: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, |
| Number of pages | 2 |
| Publisher | ACM - Association for Computing Machinery |
| Publication date | 05.06.2019 |
| Pages | 307-308 |
| ISBN (Electronic) | 9781450362320 |
| DOIs | |
| Publication status | Published - 05.06.2019 |
| MoE publication type | A4 Article in conference proceedings |
| Event | ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2019 - Rhodes, Greece Duration: 05.06.2019 → 07.06.2019 Conference number: 12 https://dl.acm.org/doi/proceedings/10.1145/3316782 |
Publication series
| Name | ACM International Conference Proceeding Series |
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Keywords
- 113 Computer and information sciences
- Edge computing
- Interactive computation
- Spiking neural networks
- 512 Business and Management