Efficient Sparse-Winograd Convolutional Neural Networks
Authors: Xingyu Liu, Jeff Pool, Song Han, William J. Dally
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For models on CIFAR-10, CIFAR-100 and Image Net datasets, our method reduces the number of multiplications by 10.4 , 6.8 and 10.8 respectively with loss of accuracy less than 0.1%, outperforming previous baselines by 2.0 -3.0 . |
| Researcher Affiliation | Collaboration | Stanford University, NVIDIA, Massachusetts Institute of Technology, Google Brain |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We open-source our code and models at https://github.com/xingyul/Sparse-Winograd-CNN. |
| Open Datasets | Yes | We used image classification datasets of different scales: CIFAR-10, CIFAR-100 (Krizhevsky & Hinton, 2009) and Image Net 2012 (Russakovsky et al., 2015). |
| Dataset Splits | No | The paper mentions using 'validation set accuracy' for CIFAR-10, CIFAR-100, and ImageNet, implying the use of a validation split. However, it does not explicitly state the specific percentages or sample counts for the training, validation, and test dataset splits, nor does it cite a specific pre-defined split methodology for all three parts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory specifications, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'TensorFlow framework' but does not provide specific version numbers for TensorFlow or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | No | The paper describes the training, pruning, and retraining phases, and mentions using the 'same data augmentation' across models. However, it does not provide specific experimental setup details such as learning rates, batch sizes, number of epochs, or optimizer settings for reproducibility. |