التفاصيل البيبلوغرافية
| العنوان: |
SGLFormer: Spiking Global-Local-Fusion Transformer with high performance |
| المؤلفون: |
Han Zhang, Chenlin Zhou, Liutao Yu, Liwei Huang, Zhengyu Ma, Xiaopeng Fan, Huihui Zhou, Yonghong Tian |
| المصدر: |
Frontiers in Neuroscience, Vol 18 (2024) |
| بيانات النشر: |
Frontiers Media S.A., 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Neurosciences. Biological psychiatry. Neuropsychiatry |
| مصطلحات موضوعية: |
Spiking Neural Network, spiking transformer, Global-Local-Fusion, Maxpooling, spatio-temporal, high performance, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571 |
| الوصف: |
IntroductionSpiking Neural Networks (SNNs), inspired by brain science, offer low energy consumption and high biological plausibility with their event-driven nature. However, the current SNNs are still suffering from insufficient performance.MethodsRecognizing the brain's adeptness at information processing for various scenarios with complex neuronal connections within and across regions, as well as specialized neuronal architectures for specific functions, we propose a Spiking Global-Local-Fusion Transformer (SGLFormer), that significantly improves the performance of SNNs. This novel architecture enables efficient information processing on both global and local scales, by integrating transformer and convolution structures in SNNs. In addition, we uncover the problem of inaccurate gradient backpropagation caused by Maxpooling in SNNs and address it by developing a new Maxpooling module. Furthermore, we adopt spatio-temporal block (STB) in the classification head instead of global average pooling, facilitating the aggregation of spatial and temporal features.ResultsSGLFormer demonstrates its superior performance on static datasets such as CIFAR10/CIFAR100, and ImageNet, as well as dynamic vision sensor (DVS) datasets including CIFAR10-DVS and DVS128-Gesture. Notably, on ImageNet, SGLFormer achieves a top-1 accuracy of 83.73% with 64 M parameters, outperforming the current SOTA directly trained SNNs by a margin of 6.66%.DiscussionWith its high performance, SGLFormer can support more computer vision tasks in the future. The codes for this study can be found in https://github.com/ZhangHanN1/SGLFormer. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1662-453X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fnins.2024.1371290/full; https://doaj.org/toc/1662-453X |
| DOI: |
10.3389/fnins.2024.1371290 |
| URL الوصول: |
https://doaj.org/article/5be57903c02540f9a45774ec4d0421c6 |
| رقم الانضمام: |
edsdoj.5be57903c02540f9a45774ec4d0421c6 |
| قاعدة البيانات: |
Directory of Open Access Journals |
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