42 citations · 159 across the 26 of their papers we have counts for
12 papers · 1 filter
QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core
Yuke Wang, Boyuan Feng, Yufei Ding
Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhea…
On Adversarial Robustness of Point Cloud Semantic Segmentation
Jiacen Xu, Zhe Zhou, Boyuan Feng +2
Recent research efforts on 3D point cloud semantic segmentation (PCSS) have achieved outstanding performance by adopting neural networks. However, the robustness of these complex m…
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…
Mitigating Noise-Induced Gradient Vanishing in Variational Quantum Algorithm Training
Anbang Wu, Gushu Li, Yufei Ding +1
Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on near-term noisy quantum computers. However, training such variational quantum algor…
Transformer Acceleration with Dynamic Sparse Attention
Liu Liu, Zheng Qu, Zhaodong Chen +2
Transformers are the mainstream of NLP applications and are becoming increasingly popular in other domains such as Computer Vision. Despite the improvements in model quality, the e…
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…
Paulihedral: A Generalized Block-Wise Compiler Optimization Framework For Quantum Simulation Kernels
Gushu Li, Anbang Wu, Yunong Shi +3
The quantum simulation kernel is an important subroutine appearing as a very long gate sequence in many quantum programs. In this paper, we propose Paulihedral, a block-wise compil…
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…
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Boyuan Feng, Yuke Wang, Tong Geng +2
Over the years, accelerating neural networks with quantization has been widely studied. Unfortunately, prior efforts with diverse precisions (e.g., 1-bit weights and 2-bit activati…
MPU: Towards Bandwidth-abundant SIMT Processor via Near-bank Computing
Xinfeng Xie, Peng Gu, Yufei Ding +3
With the growing number of data-intensive workloads, GPU, which is the state-of-the-art single-instruction-multiple-thread (SIMT) processor, is hindered by the memory bandwidth wal…
DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions
Yuke Wang, Boyuan Feng, Yufei Ding
As the key advancement of the convolutional neural networks (CNNs), depthwise separable convolutions (DSCs) are becoming one of the most popular techniques to reduce the computatio…