20 citations · 74 across the 14 of their papers we have counts for
6 papers · 1 filter
Event-Driven Learning for Spiking Neural Networks
Wenjie Wei, Malu Zhang, Jilin Zhang +7
Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on ne…
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Qianhui Liu, Jiaqi Yan, Malu Zhang +2
Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. H…
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
Qu Yang, Malu Zhang, Jibin Wu +2
The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biol…
Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate
Pengfei Sun, Jibin Wu, Malu Zhang +2
Recurrent Neural Networks (RNNs) are widely recognized for their proficiency in modeling temporal dependencies, making them highly prevalent in sequential data processing applicati…
Training Spiking Neural Networks with Local Tandem Learning
Qu Yang, Jibin Wu, Malu Zhang +3
Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient over their predecessors. However, there is a lack of an efficient and generalized tr…
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
Jibin Wu, Chenglin Xu, Daquan Zhou +2
Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency, due to their event-…