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 [1].
TETRIS: TilE-matching the TRemendous Irregular Sparsity
Authors: Yu Ji, Ling Liang, Lei Deng, Youyang Zhang, Youhui Zhang, Yuan Xie
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on three networks of different scales: Le Net on MNIST, VGG14 on CIFAR-10, and VGG16 on Image Net. The ๏ฌrst two models are trained from scratch to get the baseline accuracy, and the last model is obtained from torchvision [29]. For retraining the pruned VGG16, we use a learning rate of 0.001 and retrain 20 epochs. In all of our tests, if the block size is larger than the channel dimension of the layer, we will reduce the block size for that layer to ensure that each layer at least has two blocks. |
| Researcher Affiliation | Academia | Yu Ji1,2,3 Ling Liang3 Lei Deng3 Youyang Zhang1 Youhui Zhang1,2 Yuan Xie3 EMAIL,EMAIL 1Department of Computer Science and Technology, Tsinghua University 2Beijing Innovation Center for Future Chip EMAIL 3Department of Electrical and Computer Engineering, University of California, Santa Barbara |
| Pseudocode | Yes | Algorithm 1 Reordering algorithm |
| Open Source Code | No | The paper mentions using a third-party open-source library ('blocksparse') but does not provide access to the authors' own source code for the methodology described. |
| Open Datasets | Yes | We test our method on three networks of different scales: Le Net on MNIST, VGG14 on CIFAR-10, and VGG16 on Image Net. The ๏ฌrst two models are trained from scratch to get the baseline accuracy, and the last model is obtained from torchvision [29]. |
| Dataset Splits | No | The paper does not explicitly provide specific training, validation, and test split percentages or sample counts for the datasets used. |
| Hardware Specification | Yes | The blocksparse library [25], an open-source GPU kernel for block sparsity, is used for the evaluation on a Titan V GPU. |
| Software Dependencies | No | The paper mentions implementing the method in Pytorch and using cu Blas as backend, but it does not specify version numbers for these software components. |
| Experiment Setup | Yes | For retraining the pruned VGG16, we use a learning rate of 0.001 and retrain 20 epochs. |