103 citations · 186 across the 5 of their papers we have counts for
5 papers
Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration
Yuhang Li, Shikuang Deng, Xin Dong +1
Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the next-generation neural networks. Conventionally, SNNs can be obtain…
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
Shikuang Deng, Yuhang Li, Shanghang Zhang +1
Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest because of their event-driven and energy-efficient characteristics. Still, it is…
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
Yuhang Li, Shikuang Deng, Xin Dong +2
Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only replacing the…
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
Yuhang Li, Ruihao Gong, Xu Tan +6
We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training…
MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing
Yuhang Li, Feng Zhu, Ruihao Gong +5
User data confidentiality protection is becoming a rising challenge in the present deep learning research. Without access to data, conventional data-driven model compression faces…