Binary Event-Driven Spiking Transformer
Authors: Honglin Cao, Zijian Zhou, Wenjie Wei, Yu Liang, Ammar Belatreche, Dehao Zhang, Malu Zhang, Yang Yang, Haizhou Li
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on static and neuromorphic datasets demonstrate that our method achieves superior performance to other binary SNNs, showcasing its potential as a compact yet high-performance model for resource-limited edge devices. |
| Researcher Affiliation | Academia | 1University of Electronic Science and Technology of China 2Northumbria University 3The Chinese University of Hong Kong, Shenzhen 4National University of Singapore EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methods using mathematical equations (e.g., Equations 1-15) and textual explanations, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The repository of this paper is available at https://github.com/Cao HLin/BESTFormer. |
| Open Datasets | Yes | In this section, we first assess the classification performance of the proposed BESTformer with the CIE method on small-scale datasets, including CIFAR [Krizhevsky et al., 2009], CIFAR10-DVS [Li et al., 2017]. Following this, we evaluate the method s performance on large-scale image dataset, Image Net-1K [Deng et al., 2009]... |
| Dataset Splits | Yes | In this section, we first assess the classification performance of the proposed BESTformer with the CIE method on small-scale datasets, including CIFAR [Krizhevsky et al., 2009], CIFAR10-DVS [Li et al., 2017]. Following this, we evaluate the method s performance on large-scale image dataset, Image Net-1K [Deng et al., 2009]... |
| Hardware Specification | No | The paper does not explicitly state the specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. It mentions 'resource-constrained edge devices' as a target, but not the experimental hardware. |
| Software Dependencies | No | The paper states 'The implementation details are provided in Supplementary Materials.' but does not list specific software dependencies with version numbers in the main text. |
| Experiment Setup | No | The paper states 'The implementation details are provided in Supplementary Materials.' but does not provide specific experimental setup details (e.g., concrete hyperparameter values, training configurations) in the main text. |