Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Faster Neural Network Training with Approximate Tensor Operations
Authors: Menachem Adelman, Kfir Levy, Ido Hakimi, Mark Silberstein
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply approximate tensor operations to single and multi-node training of MLP and CNN networks on MNIST, CIFAR-10 and Image Net 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. |
| Researcher Affiliation | Collaboration | Menachem Adelman Intel & Technion EMAIL Kfir Y. Levy Technion EMAIL Ido Hakimi Technion EMAIL Mark Silberstein Technion EMAIL |
| Pseudocode | No | The provided text does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 2https://github.com/acsl-technion/approx |
| Open Datasets | Yes | We evaluate our approximate training technique on several network architectures and datasets: MLP and CNN on MNIST [28], Wide Res Net 28-10 [29] on CIFAR-10 [30], and Res Net-50 and Res Net-152 [31] on Image Net [32]. |
| Dataset Splits | Yes | We evaluate our approximate training technique on several network architectures and datasets: MLP and CNN on MNIST [28], Wide Res Net 28-10 [29] on CIFAR-10 [30], and Res Net-50 and Res Net-152 [31] on Image Net [32]. We apply approximations only during training, and use exact computations for validation/test evaluation. |
| Hardware Specification | Yes | We train the networks on a single node using NVidia V100 GPUs (two GPUs for Res Net-152, one for the rest) |
| Software Dependencies | No | We implement our techniques in Py Torch [27]... The paper mentions Py Torch but does not specify a version number for it or any other software dependency. |
| Experiment Setup | No | The paper states that training was done 'without changing training hyper-parameters' but does not explicitly list or detail these parameters (e.g., learning rate, batch size, epochs, optimizer settings) within the provided text. |