Comparing GPU and TPU in an Iterative Scenario: A Study on Neural Network-based Image Generation
| dc.contributor.author | Lehmann, Roman | |
| dc.contributor.author | Schaarschmidt, Paul | |
| dc.contributor.author | Karl, Wolfgang | |
| dc.date.accessioned | 2024-09-25T11:27:24Z | |
| dc.date.available | 2024-09-25T11:27:24Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | This paper explores the utilization of TPUs (Tensor Processing Units) and GPUs (Graphics Processing Units) in iterative applications involving neural networks. We employ a Pix2Pix approachfor computing sequential flows, evaluating the effectiveness in scenarios where NNs are only a component of the system. While TPUs demonstrate performance improvements during training with large batch sizes, we observe no significant acceleration during inference compared to GPUs. The study highlights the need to carefully consider workload and system architecture when incorporating TPUs, emphasizing that their advantages are more prominent in training tasks. | en |
| dc.identifier.issn | 0177-0454 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/44645 | |
| dc.language.iso | en | |
| dc.pubPlace | Aachen | |
| dc.publisher | Gesellschaft für Informatik e.V., Fachgruppe PARS | |
| dc.relation.ispartof | PARS-Mitteilungen: Vol. 36 | |
| dc.title | Comparing GPU and TPU in an Iterative Scenario: A Study on Neural Network-based Image Generation | en |
| dc.type | Text/Journal Article | |
| mci.reference.pages | 79-88 |
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