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Found 5 papers, 4 papers with code
SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks
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.
LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model
Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in a more biological-inspired and energy-efficient manner.
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
The experimental results show that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets.
Reducing ANN-SNN Conversion Error through Residual Membrane Potential
Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips.
Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks
Spiking Neural Networks (SNNs) have attracted significant attention due to their energy-efficient properties and potential application on neuromorphic hardware.
