51 citations · 58 across the 11 of their papers we have counts for
5 papers · 1 filter
Enhanced Self-Distillation Framework for Efficient Spiking Neural Network Training
Xiaochen Zhao, Chengting Yu, Kairong Yu +2
Spiking Neural Networks (SNNs) exhibit exceptional energy efficiency on neuromorphic hardware due to their sparse activation patterns. However, conventional training methods based…
STI-SNN: A 0.14 GOPS/W/PE Single-Timestep Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design
Kainan Wang, Chengyi Yang, Chengting Yu +3
Brain-inspired Spiking Neural Networks (SNNs) have attracted attention for their event-driven characteristics and high energy efficiency. However, the temporal dependency and irreg…
Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks
Kairong Yu, Chengting Yu, Tianqing Zhang +5
Spiking Neural Networks (SNNs), inspired by the human brain, offer significant computational efficiency through discrete spike-based information transfer. Despite their potential t…
Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacement
Shu Yang, Chengting Yu, Lei Liu +3
Spiking Neural Networks (SNNs) have garnered considerable attention as a potential alternative to Artificial Neural Networks (ANNs). Recent studies have highlighted SNNs' potential…
Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment
Chengting Yu, Xiaochen Zhao, Lei Liu +4
Spiking Neural Networks (SNNs) are emerging as a brain-inspired alternative to traditional Artificial Neural Networks (ANNs), prized for their potential energy efficiency on neurom…