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Found 7 papers, 4 papers with code
Training-free Conversion of Pretrained ANNs to SNNs for Low-Power and High-Performance Applications
This showcases its applicability to both classification and regression tasks.
Enhancing Adversarial Robustness in SNNs with Sparse Gradients
In this paper, we propose a novel approach to enhance the robustness of SNNs through gradient sparsity regularization.
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs.
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.
Optimized Potential Initialization for Low-latency Spiking Neural Networks
We evaluate our algorithm on the CIFAR-10, CIFAR-100 and ImageNet datasets and achieve state-of-the-art accuracy, using fewer time-steps.
