1 citations · 1 across the 4 of their papers we have counts for
4 papers
Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
Tuo Zhang, Ning Li, Xin Yuan +4
With the breakthrough progress of large language models (LLMs) in natural language processing and multimodal tasks, efficiently deploying them on resource-constrained edge devices…
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization
Haonan Wang, Zeli Liu, Kajimusugura Hoshino +3
Federated Learning (FL) enables distributed training on edge devices but faces significant challenges due to resource constraints in edge environments, impacting both communication…
The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities
Ning Li, Song Guo, Tuo Zhang +5
The powerfulness of LLMs indicates that deploying various LLMs with different scales and architectures on end, edge, and cloud to satisfy different requirements and adaptive hetero…
High Efficiency Inference Accelerating Algorithm for NOMA-based Mobile Edge Computing
Xin Yuan, Ning Li, Tuo Zhang +4
Splitting the inference model between device, edge server, and cloud can improve the performance of EI greatly. Additionally, the non-orthogonal multiple access (NOMA), which is th…