Edge computing systems need to support diverse sensing workloads under tight energy and memory constraints, thereby motivating deployment-aware model selection. Spiking neural networks (SNNs) are a promising alternative to conventional artificial neural networks (ANNs), yet systematic evidence for when and why they pro...
Xin Du, Di Yu, Chang-Ze Lv et al.· 2 citations· ⚡1
Edge intelligence requires models to sense continuously in real time and to keep adapting on-device, all under tight compute, energy, and memory budgets. Although spiking neural networks (SNNs) enable efficient event-driven inference, standard surrogate-gradient backpropagation (BP) serializes updates and blocks ongoin...
Yan-Xun Zhang, Yi-Fei Wang, Chang-Ze Lv et al.· 0 citations
Spiking forecasting framework SpikeLite is introduced, a spiking forecasting framework built around two modules: a Frequency-Selective Spiking Encoder for frequency-sensitive temporal encoding and a Sparse Spiking Channel Attention (SSCA) module for selective cross-channel interaction.
Bang Hu, Chang-Ze Lv, Ming-Jie Li et al.· 0 citations
This work introduces a Temporal Feedback Coding (TFC) scheme that leverages feedback at the encoding stage to diversify spike patterns and designs a Global-Local Dynamic LIF (GLD-LIF) neuron that enhances cross-step dependency modeling by integrating local aggregation and global initialization.
