Multi-GPU Training of ConvNets
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Abstract (arXiv)
In this work we evaluate different approaches to parallelize computation of convolutional neural networks across several GPUs.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Dec 2013.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 2.
6 papers.
- 2021Federated Learning on Non-IID Data: A Surveycited by 345
- 2021Communication-efficient SGD: From Local SGD to One-Shot Averagingcited by 19
- 2022PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networkscited by 1
- 2022Differentiable programming: Generalization, characterization and limitations of deep learningcited by 0
- 2023Exploring shared memory architectures for end-to-end gigapixel deep learningcited by 0
- 2023A Survey From Distributed Machine Learning to Distributed Deep Learningcited by 0
- 2023A Survey From Distributed Machine Learning to Distributed Deep Learning11 Jul 2023
- 2023Exploring shared memory architectures for end-to-end gigapixel deep learning24 Apr 2023
- 2022PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks17 Oct 2022
- 2022Differentiable programming: Generalization, characterization and limitations of deep learning13 May 2022
- 2021Federated Learning on Non-IID Data: A Survey12 Jun 2021
- 2021Communication-efficient SGD: From Local SGD to One-Shot Averaging9 Jun 2021