2 citations · 4 across the 3 of their papers we have counts for
4 papers
Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers
Yongqi Ding, Lin Zuo, Mengmeng Jing +3
Brain-inspired spiking neural networks (SNNs) promise to be a low-power alternative to computationally intensive artificial neural networks (ANNs), although performance gaps persis…
Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
Yongqi Ding, Lin Zuo, Mengmeng Jing +2
Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks that sha…
Multi-Bit Mechanism: A Novel Information Transmission Paradigm for Spiking Neural Networks
Yongjun Xiao, Xianlong Tian, Yongqi Ding +3
Since proposed, spiking neural networks (SNNs) gain recognition for their high performance, low power consumption and enhanced biological interpretability. However, while bringing…
Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network
Yongqi Ding, Lin Zuo, Mengmeng Jing +2
Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of low-power neuromorphic computing. However, existing SNNs suffer from significant latency,…