Reversible designs for extreme memory cost reduction of CNN training
Abstract Training Convolutional Neural Networks (CNN) is a resource-intensive task that requires specialized hardware for efficient computation. One of the most limiting bottlenecks of CNN training is the memory cost associated with storing the activation values of hidden layers. These values are ne...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
SpringerOpen
2023-01-01
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| Serie: | EURASIP Journal on Image and Video Processing |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1186/s13640-022-00601-w |
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