18 citations · 45 across the 5 of their papers we have counts for
5 papers
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
Yuwei Hu, Zihao Ye, Minjie Wang +6
Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is a…
Is Network the Bottleneck of Distributed Training?
Zhen Zhang, Chaokun Chang, Haibin Lin +3
Recently there has been a surge of research on improving the communication efficiency of distributed training. However, little work has been done to systematically understand wheth…
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach
Animesh Jain, Shoubhik Bhattacharya, Masahiro Masuda +2
A growing number of applications implement predictive functions using deep learning models, which require heavy use of compute and memory. One popular technique for increasing reso…
Optimizing Memory-Access Patterns for Deep Learning Accelerators
Hongbin Zheng, Sejong Oh, Huiqing Wang +7
Deep learning (DL) workloads are moving towards accelerators for faster processing and lower cost. Modern DL accelerators are good at handling the large-scale multiply-accumulate o…
A Unified Optimization Approach for CNN Model Inference on Integrated GPUs
Leyuan Wang, Zhi Chen, Yizhi Liu +4
Modern deep learning applications urge to push the model inference taking place at the edge devices for multiple reasons such as achieving shorter latency, relieving the burden of…