51 citations · 58 across the 10 of their papers we have counts for
12 papers
Proofs and disproofs of some conjectures on zero-sum problems of new types
Yang Li, San Ling, Zhonghua Sun +1
For integers and , let be the least such that every sequence of vectors in , each nonzero modulo , contains terms whose sum is zero mod…
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 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…
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…
Decoupling Dark Knowledge via Block-wise Logit Distillation for Feature-level Alignment
Chengting Yu, Fengzhao Zhang, Ruizhe Chen +4
Knowledge Distillation (KD), a learning manner with a larger teacher network guiding a smaller student network, transfers dark knowledge from the teacher to the student via logits…
Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
Chengting Yu, Lei Liu, Gaoang Wang +2
Recent insights have revealed that rate-coding is a primary form of information representation captured by surrogate-gradient-based Backpropagation Through Time (BPTT) in training…
SDiT: Spiking Diffusion Model with Transformer
Shu Yang, Hanzhi Ma, Chengting Yu +2
Spiking neural networks (SNNs) have low power consumption and bio-interpretable characteristics, and are considered to have tremendous potential for energy-efficient computing. How…
Go beyond End-to-End Training: Boosting Greedy Local Learning with Context Supply
Chengting Yu, Fengzhao Zhang, Hanzhi Ma +2
Traditional end-to-end (E2E) training of deep networks necessitates storing intermediate activations for back-propagation, resulting in a large memory footprint on GPUs and restric…
STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks
Chengting Yu, Zheming Gu, Da Li +3
Spiking Neural Networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal information processing…
MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks
Chengting Yu, Yangkai Du, Mufeng Chen +3
Spiking Neural Network (SNN) is considered more biologically realistic and power-efficient as it imitates the fundamental mechanism of the human brain. Recently, backpropagation (B…