21 citations · 59 across the 14 of their papers we have counts for
5 papers · 1 filter
TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
Yuke Wang, Boyuan Feng, Zheng Wang +2
Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e.g., e-commerce). However, the performance o…
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
Hengrui Zhang, Zhongming Yu, Guohao Dai +4
Graph Neural Networks (GNNs) have been widely used in various domains, and GNNs with sophisticated computational graph lead to higher latency and larger memory consumption. Optimiz…
LightSeq2: Accelerated Training for Transformer-based Models on GPUs
Xiaohui Wang, Yang Wei, Ying Xiong +5
Transformer-based neural models are used in many AI applications. Training these models is expensive, as it takes huge GPU resources and long duration. It is challenging because ty…
Efficient Sparse Matrix Kernels based on Adaptive Workload-Balancing and Parallel-Reduction
Guyue Huang, Guohao Dai, Yu Wang +2
Sparse matrix-vector and matrix-matrix multiplication (SpMV and SpMM) are fundamental in both conventional (graph analytics, scientific computing) and emerging (sparse DNN, GNN) do…
Machine Learning for Electronic Design Automation: A Survey
Guyue Huang, Jingbo Hu, Yifan He +13
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in…