5 papers
Frequency Matching in Spiking Neural Networks for mmWave Sensing
Di Yu, Zhenyu Liao, Changze Lv +7
Millimeter-wave (mmWave) sensing enables privacy-preserving, always-on edge perception, but its measurements are often sparse, temporally irregular, and corrupted by high-frequency…
SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network
Huijing Zhang, Muyang Cao, Linshan Jiang +4
Continuous learning of novel classes is crucial for edge devices to preserve data privacy and maintain reliable performance in dynamic environments. However, the scenario becomes p…
Edge Intelligence with Spiking Neural Networks
Shuiguang Deng, Di Yu, Changze Lv +10
The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditio…
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning
Di Yu, Xin Du, Linshan Jiang +2
The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligen…
ECC-SNN: Cost-Effective Edge-Cloud Collaboration for Spiking Neural Networks
Di Yu, Changze Lv, Xin Du +5
Most edge-cloud collaboration frameworks rely on the substantial computational and storage capabilities of cloud-based artificial neural networks (ANNs). However, this reliance res…