@inproceedings{6a919effdcc840f6bbe74f3f0eb48e0f,
title = "Sparse Spatio-Temporal Neural Network for Large-Scale Forecasting",
abstract = "We introduce sSTNN, a sparse and parallelized version of a spatio-temporal neural network (STNN) that enables training on much larger datasets. First, we introduce the model architecture and discuss the modifications we made to enable the use of a sparse data structure and multi-GPU parallelization. Then we present empirical results that demonstrate sSTNNs ability to train and inference on a dataset 17 times larger than STNN is capable of. Finally, we discuss the effect of sparsification on runtime and present evidence that sSTNN can achieve upwards of 117× reduction in memory usage compared to STNN.",
author = "Eamon Bracht and Volodymyr Kindratenko and Brunner, {Robert J.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",
year = "2022",
doi = "10.1109/BigData55660.2022.10036330",
language = "English (US)",
series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",
booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
address = "United States",
}