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..
SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN
Authors: Kang You, Zekai Xu, Chen Nie, Zhijie Deng, Qinghai Guo, Xiang Wang, Zhezhi He
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments, 4.2. Results Comparison, Table 5. Comparison on Image Net. |
| Researcher Affiliation | Collaboration | 1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China 2Huawei Technologies, Shenzhen, China. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is publicly available at: https://github.com/IntelligentComputing-Research-Group/SpikeZIP-TF |
| Open Datasets | Yes | Vision Benchmarks. Various vision datasets are adopted for evaluation. 1) static vision datasets, including CIFAR10/100 (Krizhevsky et al., 2009) and Image Net (Deng et al., 2009). 2) neuromorphic vision dataset: We evaluate Spike ZIP-TF on CIFAR10-DVS (Hongmin et al., 2017). |
| Dataset Splits | No | The paper lists the datasets used (e.g., Image Net, CIFAR10/100) but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages or sample counts) within the main text or appendices for reproducibility. |
| Hardware Specification | No | The paper mentions 'Nvidia' GPUs and 'GPU Time(h)' in Table 7 regarding training cost, but does not specify exact GPU models (e.g., A100, V100), CPU models, or other specific hardware configurations used for running experiments. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') used for the experiments. |
| Experiment Setup | Yes | The paper includes detailed hyperparameter configurations in Appendix A4, such as in Table A3 'The hyperparameter of end-to-end finetuning with Vi T-S Re LU on Image Net', listing 'optimizer Adam W', 'base learning rate 1e-4', 'batch size 64/256', 'training epochs 100', etc. |