Towards Efficient and Accurate Winograd Convolution via Full Quantization
Authors: Tianqi Chen, Weixiang Xu, Weihan Chen, Peisong Wang, Jian Cheng
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the effectiveness of our method, e.g., with 8-bit quantization and a tile size of 6, our method outperforms the previous Winograd PTQ method by 8.27% and 5.38% in terms of the top-1 accuracy on Res Net-18 and Res Net-34, respectively. |
| Researcher Affiliation | Collaboration | Tianqi Chen1,2, Weixiang Xu1, Weihan Chen1, Peisong Wang1,2,3 , Jian Cheng1,2,3,4 1Institute of Automation, Chinese Academy of Sciences 2School of Artificial Intelligence, University of Chinese Academy of Sciences 3AIRIA 4Maicro.ai {chentianqi2023, xuweixiang2018,chenweihan2018}@ia.ac.cn, {peisong.wang,jcheng}@nlpr.ia.ac.cn |
| Pseudocode | No | The paper does not contain a block or figure explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any explicit statement about open-source code availability or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Experiments demonstrate the effectiveness of our method, e.g., with 8-bit quantization and a tile size of 6, our method outperforms the previous Winograd PTQ method by 8.27% and 5.38% in terms of the top-1 accuracy on Res Net-18 and Res Net-34, respectively. |
| Dataset Splits | No | The paper mentions 'Image Net validation accuracy' in Table 6, implying the use of a validation set, and states 'we use 1024 unlabeled images' for calibration, but it does not provide specific details on dataset split percentages or counts for training, validation, or test sets. |
| Hardware Specification | Yes | All our experiments are conducted on NVIDIA Ge Force RTX 3090 24GB GPU servers and last for several hours. |
| Software Dependencies | No | The paper mentions software components like 'Adam [39] optimizer' and refers to existing quantization methods (e.g., LSQ [14], Adaround [18]) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Following BRECQ [19], we use 1024 unlabeled images and Adam [39] optimizer with 20k iterations and a batch size of 32. Based on it, we further verify our method on the strong baseline. The learning rates of A, B, and G are set to (1e-4, 1e-4, 5e-4) by default... |