The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
ImageNet Training in Minutes
Abstract
In this paper, we investigate large scale computers' capability of speeding up deep neural networks (DNN) training. Our approach is to use large batch size, powered by the Layer-wise Adaptive Rate Scaling (LARS) algorithm, for efficient usage of massive computing resources. Our approach is generic, as we empirically evaluate the effectiveness on two neural networks: AlexNet and ResNet-50 trained with the ImageNet-1k dataset while preserving the state-of-the-art test accuracy. Compared to the baseline of a previous study from a group of researchers at Facebook, our approach shows higher test accuracy on batch sizes that are larger than 16K. Using 2,048 Intel Xeon Platinum 8160 processors, we reduce the 100-epoch AlexNet training time from hours to 11 minutes. With 2,048 Intel Xeon Phi 7250 Processors, we reduce the 90-epoch ResNet-50 training time from hours to 20 minutes. Our implementation is open source and has been released in the Intel distribution of Caffe v1.0.7.
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Cites
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Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
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OpenAI, :, Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.06680 · Cited by 1,065
Efficient large-scale language model training on GPU clusters using megatron-LM
Narayanan, D., Shoeybi, M., Casper, J., LeGresley, P., Patwary, M., Korthikanti, V., Vainbrand, D., Kashinkunti, P., Bernauer, J., Catanzaro, B., et al. (2021) · Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis · doi:10.1145/3458817.3476209 · Cited by 639
Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19
Ibrahim, A. U., Ozsoz, M., Serte, S., Al-Turjman, F., & Yakoi, P. S. (2024) · Cognitive Computation · doi:10.1007/s12559-020-09787-5 · Cited by 419
MegDet: A Large Mini-Batch Object Detector
Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., Yu, G., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00647 · Cited by 311
Single chip photonic deep neural network with accelerated training
Bandyopadhyay, S., Sludds, A., Krastanov, S., Hamerly, R., Harris, N., Bunandar, D., Streshinsky, M., Hochberg, M., & Englund, D. (2022) · arXiv · doi:10.48550/arxiv.2208.01623 · Cited by 265
Metadata
- Authors
- You, Y., Zhang, Z., Hsieh, C.-J., Demmel, J., & Keutzer, K.
- Venue
- Proceedings of the 47th International Conference on Parallel Processing
- Publisher
- ACM
- Published
- 2018-08-13
- Pages
- 1-10
- ISSN
- 0190-3918 (ISSN-L)
- OpenAlex
- W2962747323
- Cited by
- 384
- References
- 27
- FWCI
- 55.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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- You, Y., Zhang, Z., Hsieh, C.-J., Demmel, J., & Keutzer, K. (2018). ImageNet Training in Minutes. Proceedings of the 47th International Conference on Parallel Processing. https://doi.org/10.1145/3225058.3225069
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