9 papers · 1 filter
UniMM-V2X: MoE-Enhanced Multi-Level Fusion for End-to-End Cooperative Autonomous Driving
Ziyi Song, Chen Xia, Chenbing Wang +3
Autonomous driving holds transformative potential but remains fundamentally constrained by the limited perception and isolated decision-making with standalone intelligence. While r…
Joint Memory Frequency and Computing Frequency Scaling for Energy-efficient DNN Inference
Yunchu Han, Zhaojun Nan, Sheng Zhou +1
Deep neural networks (DNNs) have been widely applied in diverse applications, but the problems of high latency and energy overhead are inevitable on resource-constrained devices. T…
FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen +3
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume…
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
Tan Chen, Jintao Yan, Yuxuan Sun +2
Federated learning (FL) is a promising paradigm for multiple devices to cooperatively train a model. When applied in wireless networks, two issues consistently affect the performan…
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification
Jintao Yan, Tan Chen, Yuxuan Sun +3
Asynchronous Federated Learning (AFL) enables distributed model training across multiple mobile devices, allowing each device to independently update its local model without waitin…
Joint Optimization of Offloading, Batching and DVFS for Multiuser Co-Inference
Yaodan Xu, Sheng Zhou, Zhisheng Niu
With the growing integration of artificial intelligence in mobile applications, a substantial number of deep neural network (DNN) inference requests are generated daily by mobile d…
Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Time
Zhaojun Nan, Yunchu Han, Sheng Zhou +1
In edge intelligence systems, deep neural network (DNN) partitioning and data offloading can provide real-time task inference for resource-constrained mobile devices. However, the…
DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysis
Yunchu Han, Zhaojun Nan, Sheng Zhou +1
The rapid development of deep neural networks (DNNs) is inherently accompanied by the problem of high computational costs. To tackle this challenge, dynamic voltage frequency scali…
SMDP-Based Dynamic Batching for Improving Responsiveness and Energy Efficiency of Batch Services
Yaodan Xu, Sheng Zhou, Zhisheng Niu
For servers incorporating parallel computing resources, batching is a pivotal technique for providing efficient and economical services at scale. Parallel computing resources exhib…