Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks

2 code implementations • ICLR 2022 • Tong Bu, Wei Fang, Jianhao Ding, Penglin Dai, Zhaofei Yu, Tiejun Huang

In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs.

Quantization

Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes

2 code implementations • 21 Feb 2023 • Zecheng Hao, Jianhao Ding, Tong Bu, Tiejun Huang, Zhaofei Yu

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

2 code implementations • 4 Feb 2023 • Zecheng Hao, Tong Bu, Jianhao Ding, Tiejun Huang, Zhaofei Yu

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.

Temporal Sequences

Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks

1 code implementation • CVPR 2023 • Tong Bu, Jianhao Ding, Zecheng Hao, Zhaofei Yu

Spiking Neural Networks (SNNs) have attracted significant attention due to their energy-efficient properties and potential application on neuromorphic hardware.

Image Classification

Optimized Potential Initialization for Low-latency Spiking Neural Networks

no code implementations • 3 Feb 2022 • Tong Bu, Jianhao Ding, Zhaofei Yu, Tiejun Huang

We evaluate our algorithm on the CIFAR-10, CIFAR-100 and ImageNet datasets and achieve state-of-the-art accuracy, using fewer time-steps.

Adversarial Robustness