2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks
Zhengjia Xu, Dingyang Lyu, Jinghui Zhang
As graphs grow larger, full-batch GNN training becomes hard for single GPU memory. Therefore, to enhance the scalability of GNN training, some studies have proposed sampling-based…
cs.AR2024★ 1 cited
DEFA: Efficient Deformable Attention Acceleration via Pruning-Assisted Grid-Sampling and Multi-Scale Parallel Processing
Yansong Xu, Dongxu Lyu, Zhenyu Li +6
Multi-scale deformable attention (MSDeformAttn) has emerged as a key mechanism in various vision tasks, demonstrating explicit superiority attributed to multi-scale grid-sampling.…
cs.AR2023★ 2 cited
SpOctA: A 3D Sparse Convolution Accelerator with Octree-Encoding-Based Map Search and Inherent Sparsity-Aware Processing
Dongxu Lyu, Zhenyu Li, Yuzhou Chen +3
Point-cloud-based 3D perception has attracted great attention in various applications including robotics, autonomous driving and AR/VR. In particular, the 3D sparse convolution (Sp…