PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration

Sangkug Lym
Sangkug Lym
Esha Choukse
Esha Choukse
Siavash Zangeneh
Siavash Zangeneh
Wei Wen
Wei Wen

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 362019.

Cited by: 3|Bibtex|Views27|
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Abstract:

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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