5 papers
Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping
Hangming Zhang, Zheng Li, Chenxiang Ma +4
Spiking neural networks (SNNs) offer advantages in computational efficiency via event-driven computing, compared to traditional artificial neural networks (ANNs). While direct trai…
Efficient Training of Spiking Neural Networks by Spike-aware Data Pruning
Chenxiang Ma, Xinyi Chen, Yujie Wu +2
Spiking neural networks (SNNs), recognized as an energy-efficient alternative to traditional artificial neural networks (ANNs), have advanced rapidly through the scaling of models…
Spatio-Temporal Decoupled Learning for Spiking Neural Networks
Chenxiang Ma, Xinyi Chen, Kay Chen Tan +1
Spiking neural networks (SNNs) have gained significant attention for their potential to enable energy-efficient artificial intelligence. However, effective and efficient training o…
Neuromorphic Sequential Arena: A Benchmark for Neuromorphic Temporal Processing
Xinyi Chen, Chenxiang Ma, Yujie Wu +2
Temporal processing is vital for extracting meaningful information from time-varying signals. Recent advancements in Spiking Neural Networks (SNNs) have shown immense promise in ef…
Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects
Chenxiang Ma, Xinyi Chen, Yanchen Li +7
Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses…