# Faster Neural Network Training with Approximate Tensor Operations Source: [Faster Neural Network Training with Approximate Tensor Operations]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:40:16.064Z Authors: Menachem Adelman, Kfir Y. Levy, Ido Hakimi, Mark Silberstein Published: 2018-05-21T14:14:14.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()