1 citations · 2 across the 10 of their papers we have counts for
11 papers
Learning While Inferring: Local and Parallel Learning for Edge SNNs across Sensing Modalities
Yanxun Zhang, Yifei Wang, Changze Lv +6
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 ne…
Benchmarking spiking neural networks across sensing modalities on edge devices
Xin Du, Di Yu, Changze Lv +12
Edge computing systems need to support diverse sensing workloads under tight energy and memory constraints, thereby motivating deployment-aware model selection. Spiking neural netw…
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…
ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization
Kaiwen Tang, Di Yu, Jiaqi Zheng +4
Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standard leaky integrate-and-fire (L…
Optimizing High-Throughput Distributed Data Pipelines for Reproducible Deep Learning at Scale
Kashish Mittal, Di Yu, Roozbeh Ketabi +3
Training massive-scale deep learning models on datasets spanning tens of terabytes presents critical challenges in hardware utilization and training reproducibility. In this paper,…
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…
Biologically Plausible Learning via Bidirectional Spike-Based Distillation
Changze Lv, Yifei Wang, Yanxun Zhang +7
Developing biologically plausible learning algorithms that can achieve performance comparable to error backpropagation remains a longstanding challenge. Existing approaches often c…
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…
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…
Dendritic Localized Learning: Toward Biologically Plausible Algorithm
Changze Lv, Jingwen Xu, Yiyang Lu +7
Backpropagation is the foundational algorithm for training neural networks and a key driver of deep learning's success. However, its biological plausibility has been challenged due…
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…