PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 362019.
State-of-the-art convolutional neural networks (CNNs) used in vision applications have large models with numerous weights. Training these models is very compute- and memory-resource intensive. Much research has been done on pruning or compressing these models to reduce the cost of inference, but little work has addressed the costs of trai...More
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