Tesi etd-10262022-215344
Link copiato negli appunti
Tipo di tesi
Corso Ordinario Secondo Livello
Autore
MANCINI, RICCARDO
URN
etd-10262022-215344
Titolo
High Performance implementation of Self Organizing Maps on multiprocessor and GPU architectures
Struttura
Cl. Sc. Sperimentali - Ingegneria
Corso di studi
INGEGNERIA - INGEGNERIA
Relatori
relatore Prof. CUCINOTTA, TOMMASO
Parole chiave
- cupy
- gp-gpu
- kohonen maps
- numpy
- python
- self-organizing maps
Data inizio appello
14/12/2022;
Disponibilità
completa
Riassunto analitico
Self Organizing Maps (SOMs) are a kind of unsupervised, shallow, artificial neural networks, built on top of the competitive learning principle and typically employed for clustering, dimensionality reduction and high-dimensional data visualization.
In this work, a novel, high-performance, and parallel implementation of SOM is designed, implemented, and evaluated on openly available datasets.
In this work, a novel, high-performance, and parallel implementation of SOM is designed, implemented, and evaluated on openly available datasets.
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