Scaling Up a Multispectral Resnet-50 to 128 GPUs
| Autor: | Alexandre Strube, Morris Riedel, Gabriele Cavallaro, Jenia Jitsev, Matthias Book, Rocco Sedona |
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| Rok vydání: | 2020 |
| Předmět: |
Earth observation
Speedup Artificial neural network Computer science business.industry Deep learning Multispectral image Volume (computing) 02 engineering and technology 010501 environmental sciences 01 natural sciences Convolutional neural network Residual neural network Data modeling Computer engineering 020204 information systems 0202 electrical engineering electronic engineering information engineering ddc:610 Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | IGARSS IEEE 1058-1061 (2020). doi:10.1109/IGARSS39084.2020.9324237 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Online event, Hawaii, 2020-09-26-2020-10-02 |
| Popis: | Similarly to other scientific domains, Deep Learning (DL) holds great promises to fulfil the challenging needs of Remote Sensing (RS) applications. However, the increase in volume, variety and complexity of acquisitions that are carried out on a daily basis by Earth Observation (EO) missions generates new processing and storage challenges within operational processing pipelines. The aim of this work is to show that High-Performance Computing (HPC) systems can speed up the training time of Convolutional Neural Networks (CNNs). Particular attention is put on the monitoring of the classification accuracy that usually degrades when using large batch sizes. The experimental results of this work show that the training of the model scales up to a batch size of 8,000, obtaining classification performances in terms of accuracy in line with those using smaller batch sizes. |
| Databáze: | OpenAIRE |
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