21 citations · 29 across the 5 of their papers we have counts for
5 papers
SparseSpikformer: A Co-Design Framework for Token and Weight Pruning in Spiking Transformer
Yue Liu, Shanlin Xiao, Bo Li +1
As the third-generation neural network, the Spiking Neural Network (SNN) has the advantages of low power consumption and high energy efficiency, making it suitable for implementati…
Enabling energy-Efficient object detection with surrogate gradient descent in spiking neural networks
Jilong Luo, Shanlin Xiao, Yinsheng Chen +1
Spiking Neural Networks (SNNs) are a biologically plausible neural network model with significant advantages in both event-driven processing and spatio-temporal information process…
SPA: Stochastic Probability Adjustment for System Balance of Unsupervised SNNs
Xingyu Yang, Mingyuan Meng, Shanlin Xiao +1
Spiking neural networks (SNNs) receive widespread attention because of their low-power hardware characteristic and brain-like signal response mechanism, but currently, the performa…
Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks
Mingyuan Meng, Xingyu Yang, Shanlin Xiao +1
Spiking Neural Network (SNN), as a brain-inspired approach, is attracting attention due to its potential to produce ultra-high-energy-efficient hardware. Competitive learning based…
High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature Learning
Mingyuan Meng, Xingyu Yang, Lei Bi +3
Spiking Neural Networks (SNNs) are brain-inspired, event-driven machine learning algorithms that have been widely recognized in producing ultra-high-energy-efficient hardware. Amon…