4 citations · 6 across the 4 of their papers we have counts for
5 papers
GACER: Granularity-Aware ConcurrEncy Regulation for Multi-Tenant Deep Learning
Yongbo Yu, Fuxun Yu, Mingjia Zhang +4
As deep learning continues to advance and is applied to increasingly complex scenarios, the demand for concurrent deployment of multiple neural network models has arisen. This dema…
A Survey of Multi-Tenant Deep Learning Inference on GPU
Fuxun Yu, Di Wang, Longfei Shangguan +3
Deep Learning (DL) models have achieved superior performance. Meanwhile, computing hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x throughput an…
Automated Runtime-Aware Scheduling for Multi-Tenant DNN Inference on GPU
Fuxun Yu, Shawn Bray, Di Wang +4
With the fast development of deep neural networks (DNNs), many real-world applications are adopting multiple models to conduct compound tasks, such as co-running classification, de…
A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities
Fuxun Yu, Di Wang, Longfei Shangguan +4
Deep Learning (DL) models have achieved superior performance in many application domains, including vision, language, medical, commercial ads, entertainment, etc. With the fast dev…
Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination
Fuxun Yu, Zirui Xu, Tong Shen +12
Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the increasing computational cost makes them very challenging to meet real-time latency and accuracy requirements. A…