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..
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 | 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. |