# SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks

arXiv: https://arxiv.org/abs/2403.14302 · HTML: https://mlsanity.com/paper/2403.14302/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [Spikformer: When Spiking Neural Network Meets Transformer](https://mlsanity.com/paper/2209.15425/) (2022)
- [Spike-driven Transformer](https://mlsanity.com/paper/2307.01694/) (2023)
- [Neuromorphic Data Augmentation for Training Spiking Neural Networks](https://mlsanity.com/paper/2203.06145/) (2022)

## Built on by (most cited first, 30 of 40)

- [A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models](https://mlsanity.com/paper/2410.07265/) (2024)
- [Spiking Vision Transformer with Saccadic Attention](https://mlsanity.com/paper/2502.12677/) (2025)
- [Quantized Spike-driven Transformer](https://mlsanity.com/paper/2501.13492/) (2025)
- [Rethinking Spiking Neural Networks from an Ensemble Learning Perspective](https://mlsanity.com/paper/2502.14218/) (2025)
- [Differential Coding for Training-Free ANN-to-SNN Conversion](https://mlsanity.com/paper/2503.00301/) (2025)
- [Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions](https://mlsanity.com/paper/2409.02111/) (2024)
- [Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model](https://mlsanity.com/paper/2409.15298/) (2024)
- [Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks](https://mlsanity.com/paper/2503.04223/) (2025)
- [Degree-Conscious Spiking Graph for Cross-Domain Adaptation](https://mlsanity.com/paper/2410.06883/) (2024)
- [Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks](https://mlsanity.com/paper/2602.03284/) (2026)
- [SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos](https://mlsanity.com/paper/2505.00394/) (2025)
- [TP-Spikformer: Token Pruned Spiking Transformer](https://mlsanity.com/paper/2603.00527/) (2026)
- [Deep Oscillatory Neural Network](https://mlsanity.com/paper/2405.03725/) (2024)
- [Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation](https://mlsanity.com/paper/2412.13610/) (2024)
- [Closed-Form Feedback-Free Learning with Forward Projection](https://mlsanity.com/paper/2501.16476/) (2025)
- [MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion](https://mlsanity.com/paper/2505.14719/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers](https://mlsanity.com/paper/2510.07924/) (2025)
- [Neural Dynamics Self-Attention for Spiking Transformers](https://mlsanity.com/paper/2603.19290/) (2026)
- [SDformerFlow: Spatiotemporal swin spikeformer for event-based optical flow estimation](https://mlsanity.com/paper/2409.04082/) (2024)
- [Spiking Neural Network Feature Discrimination Boosts Modality Fusion](https://mlsanity.com/paper/2502.10423/) (2025)
- [Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks](https://mlsanity.com/paper/2504.20445/) (2025)
- [SNNSIR: A Simple Spiking Neural Network for Stereo Image Restoration](https://mlsanity.com/paper/2508.12271/) (2025)
- [Toward Efficient Deep Spiking Neuron Networks:A Survey On Compression](https://mlsanity.com/paper/2407.08744/) (2024)
- [Deep activity propagation via weight initialization in spiking neural networks](https://mlsanity.com/paper/2410.00580/) (2024)
- [Rethinking SNN Online Training and Deployment: Gradient-Coherent Learning via Hybrid-Driven LIF Model](https://mlsanity.com/paper/2410.07547/) (2024)
- [Convolutional Transformer Neural Collaborative Filtering](https://mlsanity.com/paper/2412.01376/) (2024)
- [Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media](https://mlsanity.com/paper/2501.03874/) (2025)
- [Learning with Spike Synchrony in Spiking Neural Networks](https://mlsanity.com/paper/2505.14841/) (2025)
- [Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields](https://mlsanity.com/paper/2507.23033/) (2025)

## Abstract (arXiv)

The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing methods propose spiking self-attention mechanisms that are compatible with SNNs, they lack reasonable scaling methods, and the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting local features. To address these challenges, we propose a novel spiking self-attention mechanism named Dual Spike Self-Attention (DSSA) with a reasonable scaling method. Based on DSSA, we propose a novel spiking Vision Transformer architecture called SpikingResformer, which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters. Experimental results show that SpikingResformer achieves higher accuracy with fewer parameters and lower energy consumption than other spiking Vision Transformer counterparts. Notably, our SpikingResformer-L achieves 79.40% top-1 accuracy on ImageNet with 4 time-steps, which is the state-of-the-art result in the SNN field.