36 citations · 43 across the 4 of their papers we have counts for
4 papers
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design
Shuwen Lu, Zhihui Zhang, Cong Guo +3
Graph neural networks (GNNs) have shown significant accuracy improvements in a variety of graph learning domains, sparking considerable research interest. To translate these accura…
VELTAIR: Towards High-Performance Multi-tenant Deep Learning Services via Adaptive Compilation and Scheduling
Zihan Liu, Jingwen Leng, Zhihui Zhang +3
Deep learning (DL) models have achieved great success in many application domains. As such, many industrial companies such as Google and Facebook have acknowledged the importance o…
ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration
Zhihui Zhang, Jingwen Leng, Shuwen Lu +5
Graph neural networks (GNNs) start to gain momentum after showing significant performance improvement in a variety of domains including molecular science, recommendation, and trans…
Architectural Implications of Graph Neural Networks
Zhihui Zhang, Jingwen Leng, Lingxiao Ma +3
Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achie…