Behemoth: A Flash-centric Training Accelerator for Extreme-scale DNNs

Authors: 

Shine Kim, Seoul National University and Samsung Electronics; Yunho Jin, Gina Sohn, Jonghyun Bae, Tae Jun Ham, and Jae W. Lee, Seoul National University

Abstract: 

The explosive expansion of Deep Neural Networks (DNN) model size expedites the need for larger memory capacity. This movement is particularly true for models in natural language processing (NLP), a dominant application of AI along with computer vision. For example, a recent extreme-scale language model GPT-3 from OpenAI has over 175 billion parameters. Furthermore, such a model mostly consists of FC layers with huge dimensions, and thus has a relatively high arithmetic intensity. In that sense, an extreme-scale language model does not suit well to the conventional HBM DRAM-based memory system that lacks capacity and offers extremely high bandwidth. For this reason, we propose to pair the neural network training accelerator with the flash-based memory system instead of the HBM DRAM-based memory system. To design the effective flash-based memory system, we optimize the existing SSD design to improve the SSD bandwidth as well as endurance. Finally, we evaluate our proposed platform, and show that Behemoth achieves 3.65× cost saving over TPU v3 and 2.05× training throughput improvement over the accelerator attached to a commercial SSD.

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BibTeX
@inproceedings {264832,
author = {Shine Kim and Yunho Jin and Gina Sohn and Jonghyun Bae and Tae Jun Ham and Jae W. Lee},
title = {Behemoth: A Flash-centric Training Accelerator for Extreme-scale DNNs},
booktitle = {19th {USENIX} Conference on File and Storage Technologies ({FAST} 21)},
year = {2021},
isbn = {978-1-939133-20-5},
pages = {371--385},
url = {https://www.usenix.org/conference/fast21/presentation/kim},
publisher = {{USENIX} Association},
month = feb,
}

Presentation Video