Szczegóły publikacji
Opis bibliograficzny
The formation of computer cluster with limited computing resources based on an arbitrary neural network / Olexander Belej, Natalia Nestor, Iryna Artyshchuk, Krzysztof PYTEL, Nataliia Spas, Maksym Polietaiev // W: 2024 IEEE 19th international conference on the Perspective technologies and methods in MEMS design (MEMSTECH) [Dokument elektroniczny] : Zozuli, Ukraine, 16-19 May, 2024 : proceedings. — Wersja do Windows. — Dane tekstowe. — Lviv : IEEE, 2024. — (International Conference on Perspective Technologies and Methods in MEMS Design ; ISSN 2573-5373). — e-ISBN: 979-8-3503-7862-7. — S. 132–136. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 136, Abstr.
Autorzy (6)
- Belej Olexander
- Nestor Natalia
- Artyshchuk Iryna
- AGHPytel Krzysztof
- Spas Nataliia
- Polietaiev Maksym
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 157934 |
|---|---|
| Data dodania do BaDAP | 2025-03-05 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MEMSTECH63437.2024.10620061 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Czasopismo/seria | International Conference on Perspective Technologies and Methods in MEMS Design |
Abstract
We studied and compared the application areas of computer clusters with limited computing resources and well-known single-board computers. Existing solutions for calculating convolutional neural networks are analyzed. The possibility of implementing convolutional neural networks on computer clusters with limited computing resources has been established. The system implements an architecture that allows you to organize and distribute convolutional neural network calculations without dividing the convolution sphere into parts with load balancing taking into account the specifics of the data and resources processed on the cluster nodes. The developed algorithm for the application of convolutional neural networks by a computer cluster with limited computing resources makes it possible to use the developed forecasting models of convolutional neural networks to perform calculations in cyberphysical systems of the Internet of Things.
