9 papers
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…
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…
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…
Improving Quantization-aware Training of Low-Precision Network via Block Replacement on Full-Precision Counterpart
Chengting Yu, Shu Yang, Fengzhao Zhang +3
Quantization-aware training (QAT) is a common paradigm for network quantization, in which the training phase incorporates the simulation of the low-precision computation to optimiz…