13 citations · 26 across the 3 of their papers we have counts for
3 papers
cs.DC2022
Multi-user Co-inference with Batch Processing Capable Edge Server
Wenqi Shi, Sheng Zhou, Zhisheng Niu +2
Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scena…
cs.IT2020★ 13 cited
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
Wenqi Shi, Sheng Zhou, Zhisheng Niu +2
In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication res…
cs.NI2019★ 13 cited
Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning
Wenqi Shi, Yunzhong Hou, Sheng Zhou +3
Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning applications, and have been widely used on mobile devices. Running DNNs on resource-constrained…