Faster Neural Network Training with Approximate Tensor Operations
| Autor: | Adelman, M., Kfir Yehuda Levy, Hakimi, I., Silberstein, M. |
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| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Statistics - Machine Learning Computer Science::Computer Vision and Pattern Recognition Computer Science - Neural and Evolutionary Computing Machine Learning (stat.ML) Neural and Evolutionary Computing (cs.NE) Machine Learning (cs.LG) |
| Zdroj: | Scopus-Elsevier |
| Popis: | We propose a novel technique for faster deep neural network training which systematically applies sample-based approximation to the constituent tensor operations, i.e., matrix multiplications and convolutions. We introduce new sampling techniques, study their theoretical properties, and prove that they provide the same convergence guarantees when applied to SGD training. We apply approximate tensor operations to single and multi-node training of MLP and CNN networks on MNIST, CIFAR-10 and ImageNet datasets. We demonstrate up to 66% reduction in the amount of computations and communication, and up to 1.37x faster training time while maintaining negligible or no impact on the final test accuracy. NeurIPS 2021 camera ready |
| Databáze: | OpenAIRE |
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